We studied the variation of image entropy before and after wavelet decomposition, the optimal number of wavelet decomposition layers, and the effect of wavelet bases and image frequency components on entropy. Numerous...We studied the variation of image entropy before and after wavelet decomposition, the optimal number of wavelet decomposition layers, and the effect of wavelet bases and image frequency components on entropy. Numerous experiments were done on typical images to calculate (using Matlab) the entropy before and after wavelet transform. It was verified that, to obtain minimal entropy, a three-layer decomposition should be adopted rather than higher orders. The result achieved by using biorthogonal wavelet decomposition is better than that of the orthogonal wavelet decomposition. The results are not directly proportional to the vanishing moment, however.展开更多
The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to impr...The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to improve visual information of the vehicle driver in low visibility conditions is put forward based on infrared and visible image fusion technique.The wavelet image confusion algorithm is adopted to decompose the image into low-frequency approximation components and high-frequency detail components.Low-frequency component contains information representing gray value differences.High-frequency component contains the detail information of the image,which is frequently represented by gray standard deviation to assess image quality.To extract feature information of low-frequency component and high-frequency component with different emphases,different fusion operators are used separately by low-frequency and high-frequency components.In the processing of low-frequency component,the fusion rule of weighted regional energy proportion is adopted to improve the brightness of the image,and the fusion rule of weighted regional proportion of standard deviation is used in all the three high-frequency components to enhance the image contrast.The experiments on image fusion of infrared and visible light demonstrate that this image fusion method can effectively improve the image brightness and contrast,and it is suitable for vision enhancement of the low-visibility images.展开更多
Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imag...Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.展开更多
A homological multi-information image fusion method was introduced for recognition of the gastric tumor pathological tissue images.The main purpose is that fewer procedures are used to provide more information and the...A homological multi-information image fusion method was introduced for recognition of the gastric tumor pathological tissue images.The main purpose is that fewer procedures are used to provide more information and the result images could be easier to be understood than any other methods.First,multi-scale wavelet transform was used to extract edge feature,and then watershed morphology was used to form multi-threshold grayscale contours.The research laid emphasis upon the homological tissue image fusion based on extended Bayesian algorithm,which fusion result images of linear weighted algorithm was used to compare with the ones of extended Bayesian algorithm.The final fusion images are shown in Fig 5.The final image evaluation was made by information entropy,information correlativity and statistics methods.It is indicated that this method has more advantages for clinical application.展开更多
基金the Natural Science Foundation of China (No. 60472037).
文摘We studied the variation of image entropy before and after wavelet decomposition, the optimal number of wavelet decomposition layers, and the effect of wavelet bases and image frequency components on entropy. Numerous experiments were done on typical images to calculate (using Matlab) the entropy before and after wavelet transform. It was verified that, to obtain minimal entropy, a three-layer decomposition should be adopted rather than higher orders. The result achieved by using biorthogonal wavelet decomposition is better than that of the orthogonal wavelet decomposition. The results are not directly proportional to the vanishing moment, however.
基金the Science and Technology Development Program of Beijing Municipal Commission of Education (No.KM201010011002)the National College Students'Scientific Research and Entrepreneurial Action Plan(SJ201401011)
文摘The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to improve visual information of the vehicle driver in low visibility conditions is put forward based on infrared and visible image fusion technique.The wavelet image confusion algorithm is adopted to decompose the image into low-frequency approximation components and high-frequency detail components.Low-frequency component contains information representing gray value differences.High-frequency component contains the detail information of the image,which is frequently represented by gray standard deviation to assess image quality.To extract feature information of low-frequency component and high-frequency component with different emphases,different fusion operators are used separately by low-frequency and high-frequency components.In the processing of low-frequency component,the fusion rule of weighted regional energy proportion is adopted to improve the brightness of the image,and the fusion rule of weighted regional proportion of standard deviation is used in all the three high-frequency components to enhance the image contrast.The experiments on image fusion of infrared and visible light demonstrate that this image fusion method can effectively improve the image brightness and contrast,and it is suitable for vision enhancement of the low-visibility images.
文摘Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.
基金Supported by the National Science Foundation of China(No.30370403 )
文摘A homological multi-information image fusion method was introduced for recognition of the gastric tumor pathological tissue images.The main purpose is that fewer procedures are used to provide more information and the result images could be easier to be understood than any other methods.First,multi-scale wavelet transform was used to extract edge feature,and then watershed morphology was used to form multi-threshold grayscale contours.The research laid emphasis upon the homological tissue image fusion based on extended Bayesian algorithm,which fusion result images of linear weighted algorithm was used to compare with the ones of extended Bayesian algorithm.The final fusion images are shown in Fig 5.The final image evaluation was made by information entropy,information correlativity and statistics methods.It is indicated that this method has more advantages for clinical application.
