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
The breeding and selection of resistant varieties is an effective way to minimize wheat Fusarium head blight(FHB)hazards,so it is important to identify and evaluate resistant varieties.The traditional resistance pheno...The breeding and selection of resistant varieties is an effective way to minimize wheat Fusarium head blight(FHB)hazards,so it is important to identify and evaluate resistant varieties.The traditional resistance phenotype identification is still largely dependent on time-consuming manual methods.In this paper,the method for evaluating FHB resistance in wheat ears was optimized based on the fusion feature wavelength images of the hyperspectral imaging system and the Faster R-CNN algorithm.The spectral data from 400-1000 nm were preprocessed by the multiple scattering correction(MSC)algorithm.Three feature wavelengths(553 nm,682 nm and 714 nm)were selected by analyzing the X-loading weights(XLW)according to the absolute value of the peaks and troughs in different principal component(PC)load coefficient curves.Then,the different fusion methods of the three feature wavelengths were explored with different weight coefficients.Faster R-CNN was trained on the fusion and RGB datasets with VGG16,AlexNet,ZFNet,and ResNet-50 networks separately.Then,the other detection models SSD,YOLOv5,YOLOv7,CenterNet,and RetinaNet were used to compare with the Faster R-CNN model.As a result,the Faster R-CNN with VGG16 was best with the mAP(mean Average Precision)ranged from 97.7%to 98.8%.The model showed the best performance for the Fusion Image-1 dataset.Moreover,the Faster R-CNN model with VGG16 achieved an average detection accuracy of 99.00%,which was 23.89%,1.21%,0.75%,0.62%,and 8.46%higher than SSD,YOLOv5,YOLOv7,CenterNet,and RetinaNet models.Therefore,it was demonstrated that the Faster R-CNN model based on the hyperspectral feature image fusion dataset proposed in this paper was feasible for rapid evaluation of wheat FHB resistance.This study provided an important detection method for ensuring wheat food security.展开更多
In order to solve complex algorithm that is difficult to achieve real-time processing of Multiband image fusion within large amount of data, a real-time image fusion system based on FPGA and multi-DSP is designed. Fiv...In order to solve complex algorithm that is difficult to achieve real-time processing of Multiband image fusion within large amount of data, a real-time image fusion system based on FPGA and multi-DSP is designed. Five-band image acquisition, image registration, image fusion and display output can be done within the system which uses FPGA as the main processor and the other three DSP as an algorithm processor. Making full use of Flexible and high-speed characteristics of FPGA, while an image fusion algorithm based on multi-wavelet transform is optimized and applied to the system. The final experimental results show that the frame rate of 15 Hz, with a resolution of 1392 × 1040 of the five-band image can be used by the system to complete processing within 41ms.展开更多
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 Natural Science Foundation of Jiangsu Province(Grant No.BK20221518)the Jiangsu Agriculture Science and Technology Innovation Fund(Grant No.CX(23)1002)。
文摘The breeding and selection of resistant varieties is an effective way to minimize wheat Fusarium head blight(FHB)hazards,so it is important to identify and evaluate resistant varieties.The traditional resistance phenotype identification is still largely dependent on time-consuming manual methods.In this paper,the method for evaluating FHB resistance in wheat ears was optimized based on the fusion feature wavelength images of the hyperspectral imaging system and the Faster R-CNN algorithm.The spectral data from 400-1000 nm were preprocessed by the multiple scattering correction(MSC)algorithm.Three feature wavelengths(553 nm,682 nm and 714 nm)were selected by analyzing the X-loading weights(XLW)according to the absolute value of the peaks and troughs in different principal component(PC)load coefficient curves.Then,the different fusion methods of the three feature wavelengths were explored with different weight coefficients.Faster R-CNN was trained on the fusion and RGB datasets with VGG16,AlexNet,ZFNet,and ResNet-50 networks separately.Then,the other detection models SSD,YOLOv5,YOLOv7,CenterNet,and RetinaNet were used to compare with the Faster R-CNN model.As a result,the Faster R-CNN with VGG16 was best with the mAP(mean Average Precision)ranged from 97.7%to 98.8%.The model showed the best performance for the Fusion Image-1 dataset.Moreover,the Faster R-CNN model with VGG16 achieved an average detection accuracy of 99.00%,which was 23.89%,1.21%,0.75%,0.62%,and 8.46%higher than SSD,YOLOv5,YOLOv7,CenterNet,and RetinaNet models.Therefore,it was demonstrated that the Faster R-CNN model based on the hyperspectral feature image fusion dataset proposed in this paper was feasible for rapid evaluation of wheat FHB resistance.This study provided an important detection method for ensuring wheat food security.
文摘In order to solve complex algorithm that is difficult to achieve real-time processing of Multiband image fusion within large amount of data, a real-time image fusion system based on FPGA and multi-DSP is designed. Five-band image acquisition, image registration, image fusion and display output can be done within the system which uses FPGA as the main processor and the other three DSP as an algorithm processor. Making full use of Flexible and high-speed characteristics of FPGA, while an image fusion algorithm based on multi-wavelet transform is optimized and applied to the system. The final experimental results show that the frame rate of 15 Hz, with a resolution of 1392 × 1040 of the five-band image can be used by the system to complete processing within 41ms.
