●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS...●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.展开更多
This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to pr...This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.展开更多
As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of u...As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is proposed.Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep learning.By building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated simultaneously.Numerical results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise.On the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements.The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.展开更多
It is crucial to maintain the safe and stable operation of distribution transformers,which constitute a key part of power systems.In the event of transformer failure,the fault type must be diagnosed in a timely and ac...It is crucial to maintain the safe and stable operation of distribution transformers,which constitute a key part of power systems.In the event of transformer failure,the fault type must be diagnosed in a timely and accurate manner.To this end,a transformer fault diagnosis method based on infrared image processing and semi-supervised learning is proposed herein.First,we perform feature extraction on the collected infrared-image data to extract temperature,texture,and shape features as the model reference vectors.Then,a generative adversarial network(GAN)is constructed to generate synthetic samples for the minority subset of labelled samples.The proposed method can learn information from unlabeled sample data,unlike conventional supervised learning methods.Subsequently,a semi-supervised graph model is trained on the entire dataset,i.e.,both labeled and unlabeled data.Finally,we test the proposed model on an actual dataset collected from a Chinese electricity provider.The experimental results show that the use of feature extraction,sample generation,and semi-supervised learning model can improve the accuracy of transformer fault classification.This verifies the effectiveness of the proposed method.展开更多
The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation(drip irrigation,sprinkler irrigation,flood irrigation).It is the first time tha...The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation(drip irrigation,sprinkler irrigation,flood irrigation).It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass.The temperature of crop and background was measured by thermal infrared image.It is necessary to get the crop background separation index(CBSIL,CBSIH),which can be used for distinguishing the crop value from the image.CBSIL and CBSIH(the temperature when the leaves are wet adequately;the temperature when the stomata of leaf is closed completely) are the threshold values.The temperature of crop ranged from CBSIL to CBSIH.Then the ICWSI was calculated based on relevant theoretical method.The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI.In order to construct the high accuracy simulation model,the samples were divided into two parts.One was used for constructing the simulation model,the other for checking the accuracy of the model.Such result of the model was concluded as:(1) As for the simulation model of soil moisture,the correlation coefficient(R2) is larger than 0.887 6,the average of relative error(Er) ranges from 13.33% to 16.88%;(2) As for the simulation model of winter wheat yield,drip irrigation(0.887 6,16.89%,-0.12),sprinkler irrigation(0.970 0,14.85%,-0.12),flood irrigation(0.969 0,18.87%,0.18),with the values of R2,Er and CRM listed in the parentheses followed by the individual term.(3) As for winter wheat biomass,drip irrigation(0.980 0,13.70%,0.13),sprinkler irrigation(0.95,13.15%,-0.14),flood irrigation(0.970 0,14.48%,-0.13),and the values in the parentheses are demonstrated the same as above.Both the CRM and Er are shown to be very low values,which points to the accuracy and reliability of the model investigated.The accuracy of model is high and reliable.The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.展开更多
Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpret...Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.展开更多
Adding colors to monochrome thermal infrared images can help observers understand the scenery better. A nonlinear color estimation method for single-band thermal infrared imagery based on kernel principal component an...Adding colors to monochrome thermal infrared images can help observers understand the scenery better. A nonlinear color estimation method for single-band thermal infrared imagery based on kernel principal component analysis (KPCA) and sparse representation was proposed. Nonlinear features of infrared image were extracted using KPCA. The relationship between image features and chromatic values was learned using sparse representation and a color estimation model was obtained. The thermal infrared images can be rendered automatically using the color estimation model. The experimental results show that the proposed method can render infrared image with an accurate color appearance. The proposed idea can also be used in other color estimation problem.展开更多
Dear Editor,3×3 Infrared imaging,generally,of low quality,plays an important role in security surveillance and target detection.In this letter,we improve the quality of infrared images by combining both hardware ...Dear Editor,3×3 Infrared imaging,generally,of low quality,plays an important role in security surveillance and target detection.In this letter,we improve the quality of infrared images by combining both hardware and software.To this end,an infrared light field imaging enhancement system is built for the first time,including a infrared light field imaging device,a large-scale infrared light field dataset(IRLF-WHU),and a progressive fusion network for infrared image enhancement(IR-PFNet).展开更多
In the methods of image thresholding segmentation,such methods based on two-dimensional(2D) histogram and optimal objective functions are important.However,when they are used for infrared image segmentation,they are w...In the methods of image thresholding segmentation,such methods based on two-dimensional(2D) histogram and optimal objective functions are important.However,when they are used for infrared image segmentation,they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level.The concept of 2D histogram shape modification is proposed,which is realized by target information prior restraint after enhancing target information using plateau histogram equalization.The formula of 2D minimum Renyi entropy is deduced for image segmentation,then the shape-modified 2D histogram is combined with four optimal objective functions(i.e.,maximum between-class variance,maximum entropy,maximum correlation and minimum Renyi entropy) respectively for the application of infrared image segmentation.Simultaneously,F-measure is introduced to evaluate the segmentation effects objectively.The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation,and after 2D histogram shape modification,the methods of optimal objective functions can overcome their original forms' deficiency and their segmentation effects are more or less improvements,where the best one is the maximum entropy method based on 2D histogram shape modification.展开更多
Since image real-time processing requires vast amount of computation and high-speed hardware,it is difficult to be implemented with general microcomputer system. In order to solve the problem,a powerful digital signal...Since image real-time processing requires vast amount of computation and high-speed hardware,it is difficult to be implemented with general microcomputer system. In order to solve the problem,a powerful digital signal processing (DSP) hardware system is proposed,which is able to meet needs of image real-time processing.There are many approaches to enhance infrared image.But only histogram equalization is discussed because it is the most common and effective way.On the basis of histogram equalization principle,the specific procedures implemented in DSP are shown.At last the experimental results are given.展开更多
Body temperature measurement is a very important task in the sow breeding process.The authors used an infrared camera to detect the temperature of the body surface of the sows,relying on calculating the average of the...Body temperature measurement is a very important task in the sow breeding process.The authors used an infrared camera to detect the temperature of the body surface of the sows,relying on calculating the average of the infrared image temperature in the ear root region.Based on the grayscale value of the target image of the infrared image and the corresponding temperature value of 180 infrared images,a G-T(Gray-Temperature)model was established by linear least squares method,which achieved temperature inversion of each pixel of the target pig.For the different growth stages and different breeds of sows,the R-square of the all established models is greater than 0.95.The average relative error of the model inversion of the body temperature was only 0.076977%.This means that the body temperature of the sows could be detected without relying on the software.Based on the G-T model,the authors design a kind of sow's ear root recognition and body surface temperature detection algorithm for different sow population scenarios.展开更多
To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)...To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.展开更多
Infrared scene simulation has extensive applications in military and civil fields. Based on a certain experimental environment,object-oriented graphics rendering engine( OGRE) is utilized to simulate a real three-dime...Infrared scene simulation has extensive applications in military and civil fields. Based on a certain experimental environment,object-oriented graphics rendering engine( OGRE) is utilized to simulate a real three-dimensional infrared complex scene. First,the target radiation of each part is calculated based on our experimental data. Then through the analysis of the radiation characteristics of targets and related material,an infrared texture library is established and the 3ds Max software is applied to establish an infrared radiation model.Finally,a real complex infrared scene is created by using the OGRE engine image rendering technology and graphic processing unit( GPU) programmable pipeline technology. The results show that the simulation images are very similar to real images and are good supplements to real data.展开更多
In order to reduce the computational complexity of searching in massive information in detecting of warhead targets, background removal is usually the first step of target detection algorithm in sequential frame image...In order to reduce the computational complexity of searching in massive information in detecting of warhead targets, background removal is usually the first step of target detection algorithm in sequential frame images. In this paper, an adaptive multi-exposure time preserving star edge small area filtering background removal algorithm is proposed, which can suppress the back-ground and preserve the target and star edges. This algorithm not only ensures the accuracy of centroid and orbit determination, but also reduces false alarm and improves tracking accuracy.展开更多
Geometric model construction is the basis of infrared image theory simulation. Taking the construction of the geometric model of one building in Harbin as an example, this paper analyzes the theoretical groundings of ...Geometric model construction is the basis of infrared image theory simulation. Taking the construction of the geometric model of one building in Harbin as an example, this paper analyzes the theoretical groundings of simplification and principles of geometric model construction of buildings. It then discusses some particular treatment methods in calculating the radiation transfer coefficient in geometric model construction using the Monte Carlo Method.展开更多
Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009–2010 was investigated in this paper using MODIS night-time thermal infrared imagery.The cloud cover in the imagery was masked out manual...Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009–2010 was investigated in this paper using MODIS night-time thermal infrared imagery.The cloud cover in the imagery was masked out manually.Level ice thickness was calculated using MODIS ice surface temperature and an ice surface heat balance equation.Weather forcing data was from the European Centre for Medium-Range Weather Forecasts(ECMWF) analyses.The retrieved ice thickness agreed reasonable well with in situ observations from two off-shore oil platforms.The overall bias and the root mean square error of the MODIS ice thickness are –1.4 cm and 3.9 cm,respectively.The MODIS results under cold conditions(air temperature < –10°C) also agree with the estimated ice growth from Lebedev and Zubov models.The MODIS ice thickness is sensitive to the changes of the sea ice and air temperature,in particular when the sea ice is relatively thin.It is less sensitive to the wind speed.Our method is feasible for the Bohai Sea operational ice thickness analyses during cold freezing seasons.展开更多
Aiming at the problem,i.e.infrared images own the characters of bad contrast ratio and fuzzy edges,a method to enhance the contrast of infrared image is given,which is based on stationary wavelet transform.After makin...Aiming at the problem,i.e.infrared images own the characters of bad contrast ratio and fuzzy edges,a method to enhance the contrast of infrared image is given,which is based on stationary wavelet transform.After making stationary wavelet transform to an infrared image,denoising is done by the proposed method of double-threshold shrinkage in detail coefficient matrixes that have high noisy intensity.For the approximation coefficient matrix with low noisy intensity,enhancement is done by the proposed method based on histogram.The enhanced image can be got by wavelet coefficient reconstruction.Furthermore,an evaluation criterion of enhancement performance is introduced.The results show that this algorithm ensures target enhancement and restrains additive Gauss white noise effectively.At the same time,its amount of calculation is small and operation speed is fast.展开更多
The development of an efficient moving target detection algorithm in IR-image sequence is considered one of the most critical research fields in modern IRST (Infrared Search and Track) systems, especially when dealing...The development of an efficient moving target detection algorithm in IR-image sequence is considered one of the most critical research fields in modern IRST (Infrared Search and Track) systems, especially when dealing with moving dim point targets. In this paper we propose a new approach in processing of the Infrared image sequence for moving dim point targets detection built on the transformation of the IR-image sequence into 4-vectors for each frame in the sequence. The results of testing the proposed approach on a set of frames having a simple single pixel target performing a different motion patterns show the validity of the approach for detecting the motion, with simplicity in calculation and low time consumption.展开更多
基金Supported by Natural Science Foundation of Fujian Province(No.2020J011084)Fujian Province Technology and Economy Integration Service Platform(No.2023XRH001)Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone Collaborative Innovation Platform(No.2022FX5)。
文摘●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.
文摘This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.
基金the National Natural Science Foundation of China(Grant Nos.61905115,62105151,62175109,U21B2033)Leading Technology of Jiangsu Basic Research Plan(Grant No.BK20192003)+2 种基金Youth Foundation of Jiangsu Province(Grant Nos.BK20190445,BK20210338)Fundamental Research Funds for the Central Universities(Grant No.30920032101)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(Grant No.JSGP202105)to provide fund for conducting experiments。
文摘As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is proposed.Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep learning.By building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated simultaneously.Numerical results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise.On the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements.The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.
基金supported by China Southern Power Grid Co.Ltd.science and technology project(Research on the theory,technology and application of stereoscopic disaster defense for power distribution network in large city,GZHKJXM20180060)National Natural Science Foundation of China(No.51477100).
文摘It is crucial to maintain the safe and stable operation of distribution transformers,which constitute a key part of power systems.In the event of transformer failure,the fault type must be diagnosed in a timely and accurate manner.To this end,a transformer fault diagnosis method based on infrared image processing and semi-supervised learning is proposed herein.First,we perform feature extraction on the collected infrared-image data to extract temperature,texture,and shape features as the model reference vectors.Then,a generative adversarial network(GAN)is constructed to generate synthetic samples for the minority subset of labelled samples.The proposed method can learn information from unlabeled sample data,unlike conventional supervised learning methods.Subsequently,a semi-supervised graph model is trained on the entire dataset,i.e.,both labeled and unlabeled data.Finally,we test the proposed model on an actual dataset collected from a Chinese electricity provider.The experimental results show that the use of feature extraction,sample generation,and semi-supervised learning model can improve the accuracy of transformer fault classification.This verifies the effectiveness of the proposed method.
基金China-Germany international cooperation project(IRTG1070)National Natural Science Foundation of China(Item number:0971940)
文摘The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation(drip irrigation,sprinkler irrigation,flood irrigation).It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass.The temperature of crop and background was measured by thermal infrared image.It is necessary to get the crop background separation index(CBSIL,CBSIH),which can be used for distinguishing the crop value from the image.CBSIL and CBSIH(the temperature when the leaves are wet adequately;the temperature when the stomata of leaf is closed completely) are the threshold values.The temperature of crop ranged from CBSIL to CBSIH.Then the ICWSI was calculated based on relevant theoretical method.The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI.In order to construct the high accuracy simulation model,the samples were divided into two parts.One was used for constructing the simulation model,the other for checking the accuracy of the model.Such result of the model was concluded as:(1) As for the simulation model of soil moisture,the correlation coefficient(R2) is larger than 0.887 6,the average of relative error(Er) ranges from 13.33% to 16.88%;(2) As for the simulation model of winter wheat yield,drip irrigation(0.887 6,16.89%,-0.12),sprinkler irrigation(0.970 0,14.85%,-0.12),flood irrigation(0.969 0,18.87%,0.18),with the values of R2,Er and CRM listed in the parentheses followed by the individual term.(3) As for winter wheat biomass,drip irrigation(0.980 0,13.70%,0.13),sprinkler irrigation(0.95,13.15%,-0.14),flood irrigation(0.970 0,14.48%,-0.13),and the values in the parentheses are demonstrated the same as above.Both the CRM and Er are shown to be very low values,which points to the accuracy and reliability of the model investigated.The accuracy of model is high and reliable.The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.
基金This work was supported by National Key R&D Program of China(2019YFE0102900).
文摘Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.
基金National Natural Science Foundation of China(No. 61072090)the Fundamental Research Funds for the Central Universities,China+2 种基金Shanghai Pujiang Program,China(No. 12PJ1402200)China Postdoctoral Science Foundation Funded Project(No. 2012M511058)Shanghai Postdoctoral Sustentation Fund,China(No. 12R21412500)
文摘Adding colors to monochrome thermal infrared images can help observers understand the scenery better. A nonlinear color estimation method for single-band thermal infrared imagery based on kernel principal component analysis (KPCA) and sparse representation was proposed. Nonlinear features of infrared image were extracted using KPCA. The relationship between image features and chromatic values was learned using sparse representation and a color estimation model was obtained. The thermal infrared images can be rendered automatically using the color estimation model. The experimental results show that the proposed method can render infrared image with an accurate color appearance. The proposed idea can also be used in other color estimation problem.
文摘Dear Editor,3×3 Infrared imaging,generally,of low quality,plays an important role in security surveillance and target detection.In this letter,we improve the quality of infrared images by combining both hardware and software.To this end,an infrared light field imaging enhancement system is built for the first time,including a infrared light field imaging device,a large-scale infrared light field dataset(IRLF-WHU),and a progressive fusion network for infrared image enhancement(IR-PFNet).
基金supported by the China Postdoctoral Science Foundation(20100471451)the Science and Technology Foundation of State Key Laboratory of Underwater Measurement&Control Technology(9140C2603051003)
文摘In the methods of image thresholding segmentation,such methods based on two-dimensional(2D) histogram and optimal objective functions are important.However,when they are used for infrared image segmentation,they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level.The concept of 2D histogram shape modification is proposed,which is realized by target information prior restraint after enhancing target information using plateau histogram equalization.The formula of 2D minimum Renyi entropy is deduced for image segmentation,then the shape-modified 2D histogram is combined with four optimal objective functions(i.e.,maximum between-class variance,maximum entropy,maximum correlation and minimum Renyi entropy) respectively for the application of infrared image segmentation.Simultaneously,F-measure is introduced to evaluate the segmentation effects objectively.The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation,and after 2D histogram shape modification,the methods of optimal objective functions can overcome their original forms' deficiency and their segmentation effects are more or less improvements,where the best one is the maximum entropy method based on 2D histogram shape modification.
文摘Since image real-time processing requires vast amount of computation and high-speed hardware,it is difficult to be implemented with general microcomputer system. In order to solve the problem,a powerful digital signal processing (DSP) hardware system is proposed,which is able to meet needs of image real-time processing.There are many approaches to enhance infrared image.But only histogram equalization is discussed because it is the most common and effective way.On the basis of histogram equalization principle,the specific procedures implemented in DSP are shown.At last the experimental results are given.
基金National Key R&D Program of China,Grant/Award Number:2017YFD0701601,Grant/Award Number:cience and Technology Support Key Project of Tianjin,Grant/Award Number:20YFZCSN00220Tianjin Agricultural University Education and Teaching Reform Research Project,Grant/Award Number:2018-B-23Major Educational Reform Project of Tianjin Agricultural University,Grant/Award Number:2017-B-03。
文摘Body temperature measurement is a very important task in the sow breeding process.The authors used an infrared camera to detect the temperature of the body surface of the sows,relying on calculating the average of the infrared image temperature in the ear root region.Based on the grayscale value of the target image of the infrared image and the corresponding temperature value of 180 infrared images,a G-T(Gray-Temperature)model was established by linear least squares method,which achieved temperature inversion of each pixel of the target pig.For the different growth stages and different breeds of sows,the R-square of the all established models is greater than 0.95.The average relative error of the model inversion of the body temperature was only 0.076977%.This means that the body temperature of the sows could be detected without relying on the software.Based on the G-T model,the authors design a kind of sow's ear root recognition and body surface temperature detection algorithm for different sow population scenarios.
基金Supported by the National Natural Science Foundation of China(60905012,60572058)
文摘To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.
基金Supported by the National Twelfth Five-Year Project(40405050303)
文摘Infrared scene simulation has extensive applications in military and civil fields. Based on a certain experimental environment,object-oriented graphics rendering engine( OGRE) is utilized to simulate a real three-dimensional infrared complex scene. First,the target radiation of each part is calculated based on our experimental data. Then through the analysis of the radiation characteristics of targets and related material,an infrared texture library is established and the 3ds Max software is applied to establish an infrared radiation model.Finally,a real complex infrared scene is created by using the OGRE engine image rendering technology and graphic processing unit( GPU) programmable pipeline technology. The results show that the simulation images are very similar to real images and are good supplements to real data.
文摘In order to reduce the computational complexity of searching in massive information in detecting of warhead targets, background removal is usually the first step of target detection algorithm in sequential frame images. In this paper, an adaptive multi-exposure time preserving star edge small area filtering background removal algorithm is proposed, which can suppress the back-ground and preserve the target and star edges. This algorithm not only ensures the accuracy of centroid and orbit determination, but also reduces false alarm and improves tracking accuracy.
文摘Geometric model construction is the basis of infrared image theory simulation. Taking the construction of the geometric model of one building in Harbin as an example, this paper analyzes the theoretical groundings of simplification and principles of geometric model construction of buildings. It then discusses some particular treatment methods in calculating the radiation transfer coefficient in geometric model construction using the Monte Carlo Method.
基金The Chinese Polar Environment Comprehensive Investigation&Assessment Programs under contract No.CHINARE-02-04the International Science and Technology Cooperation Project of China under contract No.2011DFA22260+3 种基金the Open Research Fund of Key Laboratory of Digital Earth Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences under contract No.2014LDE009the Public Science and Technology Research Funds Projects of Ocean under contract No.201105016the Academy of Finland under contract No.259537the National Natural Science Foundation of China under contract No.41428603
文摘Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009–2010 was investigated in this paper using MODIS night-time thermal infrared imagery.The cloud cover in the imagery was masked out manually.Level ice thickness was calculated using MODIS ice surface temperature and an ice surface heat balance equation.Weather forcing data was from the European Centre for Medium-Range Weather Forecasts(ECMWF) analyses.The retrieved ice thickness agreed reasonable well with in situ observations from two off-shore oil platforms.The overall bias and the root mean square error of the MODIS ice thickness are –1.4 cm and 3.9 cm,respectively.The MODIS results under cold conditions(air temperature < –10°C) also agree with the estimated ice growth from Lebedev and Zubov models.The MODIS ice thickness is sensitive to the changes of the sea ice and air temperature,in particular when the sea ice is relatively thin.It is less sensitive to the wind speed.Our method is feasible for the Bohai Sea operational ice thickness analyses during cold freezing seasons.
基金the Aeronautics Science Foundation of China(20070153005)Astronautics Science Technology Innovation Foundation of China(05C53005)
文摘Aiming at the problem,i.e.infrared images own the characters of bad contrast ratio and fuzzy edges,a method to enhance the contrast of infrared image is given,which is based on stationary wavelet transform.After making stationary wavelet transform to an infrared image,denoising is done by the proposed method of double-threshold shrinkage in detail coefficient matrixes that have high noisy intensity.For the approximation coefficient matrix with low noisy intensity,enhancement is done by the proposed method based on histogram.The enhanced image can be got by wavelet coefficient reconstruction.Furthermore,an evaluation criterion of enhancement performance is introduced.The results show that this algorithm ensures target enhancement and restrains additive Gauss white noise effectively.At the same time,its amount of calculation is small and operation speed is fast.
文摘The development of an efficient moving target detection algorithm in IR-image sequence is considered one of the most critical research fields in modern IRST (Infrared Search and Track) systems, especially when dealing with moving dim point targets. In this paper we propose a new approach in processing of the Infrared image sequence for moving dim point targets detection built on the transformation of the IR-image sequence into 4-vectors for each frame in the sequence. The results of testing the proposed approach on a set of frames having a simple single pixel target performing a different motion patterns show the validity of the approach for detecting the motion, with simplicity in calculation and low time consumption.
