As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy gro...As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy ground or a white wall), resulting in underestimation of the transmittance of some local scenes. To address that problem, we propose an image dehazing method by incorporating Markov random field(MRF) with the DCP. The DCP explicitly represents the input image observation in the MRF model obtained by the transmittance map. The key idea is that the sparsely distributed wrongly estimated transmittance can be corrected by properly characterizing the spatial dependencies between the neighboring pixels of the transmittances that are well estimated and those that are wrongly estimated. To that purpose, the energy function of the MRF model is designed. The estimation of the initial transmittance map is pixel-based using the DCP, and the segmentation on the transmittance map is employed to separate the foreground and background, thereby avoiding the block effect and artifacts at the depth discontinuity. Given the limited number of labels obtained by clustering, the smoothing term in the MRF model can properly smooth the transmittance map without an extra refinement filter. Experimental results obtained by using terrestrial and underwater images are given.展开更多
The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical ap- plications. This paper is...The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical ap- plications. This paper is based on the dark channel prior principle and aims at the prior information absent blurred image degradation situation. A lot of improvements have been made to estimate the transmission map of blurred images. Since the dark channel prior principle can effectively restore the blurred image at the cost of a large amount of computation, the total variation （TV） and image morphology transform （specifically top-hat transform and bottom- hat transform） have been introduced into the improved method. Compared with original transmission map estimation methods, the proposed method features both simplicity and accuracy. The es- timated transmission map together with the element can restore the image. Simulation results show that this method could inhibit the ill-posed problem during image restoration, meanwhile it can greatly improve the image quality and definition.展开更多
Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spa...Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spatial correlation of dark channel prior. Secondly, a degradation model is utilized to restore the foggy image. Thirdly, the final recovered image, with enhanced contrast, is obtained by performing a post-processing technique based on just-noticeable difference. Experimental results demonstrate that the information of a foggy image can be recovered perfectly by the proposed method, even in the case of the abrupt depth changing scene.展开更多
Aiming at the inaccurate transmission estimation problem of dark channel prior image dehazing algorithm in the sudden change area of depth of field and sky area,a dehazing algorithm using adaptive dark channel fusion ...Aiming at the inaccurate transmission estimation problem of dark channel prior image dehazing algorithm in the sudden change area of depth of field and sky area,a dehazing algorithm using adaptive dark channel fusion and sky compensation is proposed.Firstly,according to the characteristics of minimum filtering of large window scale and small window scale in the dark channel prior,the fused dark channel is obtained by weighted fusion of the approximate depth of field relationship,thus obtaining the primary transmission.Secondly,use the down-sampling to optimize the primary transmission combined with gray scale image of haze image by fast joint bilateral filtering,then restore the original image size by up-sampling,and the compensation of the Gaussian function is used in the sky area to obtain corrected transmission.Finally,the improved atmospheric light is combined with atmospheric scattering model to recover haze-free image.Experimental results show that the algorithm can recover a large amount of detailed information of the image,obtain high visibility,and effectively eliminate the halo effect.At the same time,it has a better recovery effect on bright areas such as the sky area.展开更多
To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing m...To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing method based on Gaussian mixture model is proposed.Firstly,we use the Gaussian mixture model to model the hazy image,and then use the expectation maximization(EM)algorithm to optimize the parameters,so that the hazy image can be divided into the sky region and the non-sky region.Secondly,the sky region is divided into a light haze region,a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively.Thirdly,the restored image is obtained by combining the atmospheric scattering model.Finally,adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image.The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region,and the restored image is clearer and has better visual effect.展开更多
In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits...In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits the efficiency of machine vision systems such as video surveillance, target tracking and recognition. Various single image dark channel dehazing algorithms have aimed to tackle the problem of image hazing in a fast and efficient manner. Such algorithms rely upon the dark channel prior theory towards the estimation of the atmospheric light which offers itself as a crucial parameter towards dehazing. This paper studies the state-of-the-art in this area and puts forwards their strengths and weaknesses. Through experiments the efficiencies and shortcomings of these algorithms are shared. This information is essential for researchers and developers in providing a reference for the development of applications and future of the research field.展开更多
The existing UAV aerial image de-fog methods have low image contrast after de-fog,the difference between light and dark image is not obvious,leading to poor de-fog effect.Therefore,an aerial image de-fog enhancement m...The existing UAV aerial image de-fog methods have low image contrast after de-fog,the difference between light and dark image is not obvious,leading to poor de-fog effect.Therefore,an aerial image de-fog enhancement method based on dark channel a priori is proposed.The image variance and absolute gradient mean are combined to get the weight coefficients,and the edge pixels are smoothed by using the multiple decomposition form.The image intensity is calculated and the noise is reduced.A convolution neural network is introduced to calculate the atmospheric transmittance in haze.Based on this,dark channel prior algorithm is used to enhance the light and shade difference of aerial photography image and realise the de-fog enhancement of aerial photography image.To verify the performance of the proposed method,simulation experiments are designed which were compared with the existing methods results in better fog-removing effect,higher contrast and shorter time.展开更多
Haze scatters light transmitted in the air and reduces the visibility of images.Dealing with haze is still a challenge for image processing applications nowadays.For the purpose of haze removal,we propose an accelerat...Haze scatters light transmitted in the air and reduces the visibility of images.Dealing with haze is still a challenge for image processing applications nowadays.For the purpose of haze removal,we propose an accelerated dehazing method based on single pixels.Unlike other methods based on regions,our method estimates the transmission map and atmospheric light for each pixel independently,so that all parameters can be evaluated in one traverse,which is a key to acceleration.Then,the transmission map is bilaterally filtered to restore the relationship between pixels.After restoration via the linear hazy model,the restored images are tuned to improve the contrast,value,and saturation,in particular to offset the intensity errors in different channels caused by the corresponding wavelengths.The experimental results demonstrate that the proposed dehazing method outperforms the state-of-the-art dehazing methods in terms of processing speed.Comparisons with other dehazing methods and quantitative criteria(peak signal-to-noise ratio,detectable marginal rate,and information entropy difference)are introduced to verify its performance.展开更多
Based on image segmentation and the dark channel prior,this paper proposes a fog removal algorithm in the HSI color space.Usually,the dark channel prior based defogging methods easily produce color distortion and halo...Based on image segmentation and the dark channel prior,this paper proposes a fog removal algorithm in the HSI color space.Usually,the dark channel prior based defogging methods easily produce color distortion and halo effect when applied on images with a large sky area,because the sky region does not meet the prior assumption.For this reason,our method presents a new threshold sky region segmentation algorithm using the initial transmission map of the intensity component I.Based on the segmentation result,the initial transmission map is modified in turn,and finally refined by the guided filter.The saturation components S is reconstructed using the low frequencies of the V-transform to reduce noise,and stretched by multiplying a constant related to the initial transmission map.Experimental results show that the proposed algorithm has low time complexity and compelling fog removal result in both visual effect and quantitative measurement.展开更多
Obtaining clear and true images is a basic requirement for agricultural monitoring.However,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulti...Obtaining clear and true images is a basic requirement for agricultural monitoring.However,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulting in the difficulty of target extraction.Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion.In order to address the above-mentioned problems caused by traditional image dehazing methods,an improved image dehazing method based on dark channel prior(DCP)was proposed.By enhancing the brightness of the hazed image and processing the sky area,the dim and un-natural problems caused by traditional image dehazing algorithms were resolved.Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm,and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method.Three image evaluation indicators including mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were used to evaluate the dehazing performance.Results showed that the PSNR and entropy with the proposed method increased by 21.81%and 5.71%,and MSE decreased by 40.07%compared with the original DCP method.It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95%and entropy by 2.04%and a decrease of MSE by 84.78%.The results from this study can provide a reference for agricultural field monitoring.展开更多
An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimat...An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimate and refine the depth map of haze images. Moreover, a contrast enhancement method based on just noticeable difference(JND) and quadratic function is adopted to enhance the contrast for the dehazed image, since the scene radiance is usually not as bright as the atmospheric light,and the dehazed image looks dim. The experimental results show that the proposed approach can effectively enhance the haze image and is well suitable for implementing on the surveillance and obstacle detection systems.展开更多
基金supported by the National Natural Science Foundation of China (No.61571407)。
文摘As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy ground or a white wall), resulting in underestimation of the transmittance of some local scenes. To address that problem, we propose an image dehazing method by incorporating Markov random field(MRF) with the DCP. The DCP explicitly represents the input image observation in the MRF model obtained by the transmittance map. The key idea is that the sparsely distributed wrongly estimated transmittance can be corrected by properly characterizing the spatial dependencies between the neighboring pixels of the transmittances that are well estimated and those that are wrongly estimated. To that purpose, the energy function of the MRF model is designed. The estimation of the initial transmittance map is pixel-based using the DCP, and the segmentation on the transmittance map is employed to separate the foreground and background, thereby avoiding the block effect and artifacts at the depth discontinuity. Given the limited number of labels obtained by clustering, the smoothing term in the MRF model can properly smooth the transmittance map without an extra refinement filter. Experimental results obtained by using terrestrial and underwater images are given.
基金supported by the National Natural Science Foundation of China(61301095)the Chinese University Scientific Fund(HEUCF130807)the Chinese Defense Advanced Research Program of Science and Technology(10J3.1.6)
文摘The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical ap- plications. This paper is based on the dark channel prior principle and aims at the prior information absent blurred image degradation situation. A lot of improvements have been made to estimate the transmission map of blurred images. Since the dark channel prior principle can effectively restore the blurred image at the cost of a large amount of computation, the total variation （TV） and image morphology transform （specifically top-hat transform and bottom- hat transform） have been introduced into the improved method. Compared with original transmission map estimation methods, the proposed method features both simplicity and accuracy. The es- timated transmission map together with the element can restore the image. Simulation results show that this method could inhibit the ill-posed problem during image restoration, meanwhile it can greatly improve the image quality and definition.
基金supported by "the Twelfth Five-year Civil Aerospace Technologies Pre-Research Program"(D040201)
文摘Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spatial correlation of dark channel prior. Secondly, a degradation model is utilized to restore the foggy image. Thirdly, the final recovered image, with enhanced contrast, is obtained by performing a post-processing technique based on just-noticeable difference. Experimental results demonstrate that the information of a foggy image can be recovered perfectly by the proposed method, even in the case of the abrupt depth changing scene.
基金National Natural Science Foundation of China(No.61561030)Natural Science Foundation of Science and Technology Department of Gansu Province(No.1310RJZA050)Basic Research Projects Supported by Operating Expenses of Finance Department of Gansu Province(No.214138)。
文摘Aiming at the inaccurate transmission estimation problem of dark channel prior image dehazing algorithm in the sudden change area of depth of field and sky area,a dehazing algorithm using adaptive dark channel fusion and sky compensation is proposed.Firstly,according to the characteristics of minimum filtering of large window scale and small window scale in the dark channel prior,the fused dark channel is obtained by weighted fusion of the approximate depth of field relationship,thus obtaining the primary transmission.Secondly,use the down-sampling to optimize the primary transmission combined with gray scale image of haze image by fast joint bilateral filtering,then restore the original image size by up-sampling,and the compensation of the Gaussian function is used in the sky area to obtain corrected transmission.Finally,the improved atmospheric light is combined with atmospheric scattering model to recover haze-free image.Experimental results show that the algorithm can recover a large amount of detailed information of the image,obtain high visibility,and effectively eliminate the halo effect.At the same time,it has a better recovery effect on bright areas such as the sky area.
基金National Natural Science Foundation of China(Nos.61841303,61963023)Project of Humanities and Social Sciences of Ministry of Education in China(No.19YJC760012)。
文摘To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing method based on Gaussian mixture model is proposed.Firstly,we use the Gaussian mixture model to model the hazy image,and then use the expectation maximization(EM)algorithm to optimize the parameters,so that the hazy image can be divided into the sky region and the non-sky region.Secondly,the sky region is divided into a light haze region,a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively.Thirdly,the restored image is obtained by combining the atmospheric scattering model.Finally,adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image.The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region,and the restored image is clearer and has better visual effect.
文摘In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits the efficiency of machine vision systems such as video surveillance, target tracking and recognition. Various single image dark channel dehazing algorithms have aimed to tackle the problem of image hazing in a fast and efficient manner. Such algorithms rely upon the dark channel prior theory towards the estimation of the atmospheric light which offers itself as a crucial parameter towards dehazing. This paper studies the state-of-the-art in this area and puts forwards their strengths and weaknesses. Through experiments the efficiencies and shortcomings of these algorithms are shared. This information is essential for researchers and developers in providing a reference for the development of applications and future of the research field.
基金The National Key Research and Development Program of China[grant numbers 2020YFC2004003 and 2020YFC2004002].
文摘The existing UAV aerial image de-fog methods have low image contrast after de-fog,the difference between light and dark image is not obvious,leading to poor de-fog effect.Therefore,an aerial image de-fog enhancement method based on dark channel a priori is proposed.The image variance and absolute gradient mean are combined to get the weight coefficients,and the edge pixels are smoothed by using the multiple decomposition form.The image intensity is calculated and the noise is reduced.A convolution neural network is introduced to calculate the atmospheric transmittance in haze.Based on this,dark channel prior algorithm is used to enhance the light and shade difference of aerial photography image and realise the de-fog enhancement of aerial photography image.To verify the performance of the proposed method,simulation experiments are designed which were compared with the existing methods results in better fog-removing effect,higher contrast and shorter time.
基金Project supported by the National Natural Science Foundation of China(Nos.U1664264 and U1509203)
文摘Haze scatters light transmitted in the air and reduces the visibility of images.Dealing with haze is still a challenge for image processing applications nowadays.For the purpose of haze removal,we propose an accelerated dehazing method based on single pixels.Unlike other methods based on regions,our method estimates the transmission map and atmospheric light for each pixel independently,so that all parameters can be evaluated in one traverse,which is a key to acceleration.Then,the transmission map is bilaterally filtered to restore the relationship between pixels.After restoration via the linear hazy model,the restored images are tuned to improve the contrast,value,and saturation,in particular to offset the intensity errors in different channels caused by the corresponding wavelengths.The experimental results demonstrate that the proposed dehazing method outperforms the state-of-the-art dehazing methods in terms of processing speed.Comparisons with other dehazing methods and quantitative criteria(peak signal-to-noise ratio,detectable marginal rate,and information entropy difference)are introduced to verify its performance.
基金Supported by the National Natural Science Foundation of China(61571046)the National Key Research and Development Program of China(2017YFF0209806)
文摘Based on image segmentation and the dark channel prior,this paper proposes a fog removal algorithm in the HSI color space.Usually,the dark channel prior based defogging methods easily produce color distortion and halo effect when applied on images with a large sky area,because the sky region does not meet the prior assumption.For this reason,our method presents a new threshold sky region segmentation algorithm using the initial transmission map of the intensity component I.Based on the segmentation result,the initial transmission map is modified in turn,and finally refined by the guided filter.The saturation components S is reconstructed using the low frequencies of the V-transform to reduce noise,and stretched by multiplying a constant related to the initial transmission map.Experimental results show that the proposed algorithm has low time complexity and compelling fog removal result in both visual effect and quantitative measurement.
基金supported by the National High Technology Research and Development Program of China(863 Program)(No.2013AA10230402)Agricultural Science and Technology Project of Shaanxi Province(No.2016NY-157)Fundamental Research Funds Central Universities(2452016077).
文摘Obtaining clear and true images is a basic requirement for agricultural monitoring.However,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulting in the difficulty of target extraction.Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion.In order to address the above-mentioned problems caused by traditional image dehazing methods,an improved image dehazing method based on dark channel prior(DCP)was proposed.By enhancing the brightness of the hazed image and processing the sky area,the dim and un-natural problems caused by traditional image dehazing algorithms were resolved.Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm,and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method.Three image evaluation indicators including mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were used to evaluate the dehazing performance.Results showed that the PSNR and entropy with the proposed method increased by 21.81%and 5.71%,and MSE decreased by 40.07%compared with the original DCP method.It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95%and entropy by 2.04%and a decrease of MSE by 84.78%.The results from this study can provide a reference for agricultural field monitoring.
基金supported by the National Natural Science Foundation of China(61075013)the Joint Funds of the Civil Aviation(61139003)
文摘An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimate and refine the depth map of haze images. Moreover, a contrast enhancement method based on just noticeable difference(JND) and quadratic function is adopted to enhance the contrast for the dehazed image, since the scene radiance is usually not as bright as the atmospheric light,and the dehazed image looks dim. The experimental results show that the proposed approach can effectively enhance the haze image and is well suitable for implementing on the surveillance and obstacle detection systems.
