A novel scheme for image data restoration is proposed in this letter. First, a window- function model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in trans...A novel scheme for image data restoration is proposed in this letter. First, a window- function model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in transform-domain. Then, an iterative algorithm is presented to solve the deconvolution. Because the window-function is available to describe arbitrary shape, our algorithm is suitable for restoring irregular segment of data loss, including square-block. Finally, several simulation tests are done and results prove that the algorithm is valid.展开更多
The traditional single image dehazing algorithm is susceptible to the prior knowledge of hazy image and colour distortion.A new method of deep learning multi-scale convolution neural network based on HSI colour space ...The traditional single image dehazing algorithm is susceptible to the prior knowledge of hazy image and colour distortion.A new method of deep learning multi-scale convolution neural network based on HSI colour space for single image dehazing is proposed in this paper,which directly learns the mapping relationship between hazy image and corresponding clear image in colour,saturation and brightness by the designed structure of deep learning network to achieve haze removal.Firstly,the hazy image is transformed from RGB colour space to HSI colour space.Secondly,an end-to-end multi-scale full convolution neural network model is designed.The multi-scale extraction is realized by three different dehazing sub-networks:hue H,saturation S and intensity I,and the mapping relationship between hazy image and clear image is obtained by deep learning.Finally,the model was trained and tested with hazy data set.The experimental results show that this method can achieve good dehazing effect for both synthetic hazy images and real hazy images,and is superior to other contrast algorithms in subjective and objective evaluations.展开更多
基金Supported by the National Natural Science Foundation of China (No.60072012).
文摘A novel scheme for image data restoration is proposed in this letter. First, a window- function model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in transform-domain. Then, an iterative algorithm is presented to solve the deconvolution. Because the window-function is available to describe arbitrary shape, our algorithm is suitable for restoring irregular segment of data loss, including square-block. Finally, several simulation tests are done and results prove that the algorithm is valid.
基金National Natural Science Foundation of China(No.61963023)MOE(Ministry of Education in China)Project of Humanities and Social Sciences(No.19YJC760012)。
文摘The traditional single image dehazing algorithm is susceptible to the prior knowledge of hazy image and colour distortion.A new method of deep learning multi-scale convolution neural network based on HSI colour space for single image dehazing is proposed in this paper,which directly learns the mapping relationship between hazy image and corresponding clear image in colour,saturation and brightness by the designed structure of deep learning network to achieve haze removal.Firstly,the hazy image is transformed from RGB colour space to HSI colour space.Secondly,an end-to-end multi-scale full convolution neural network model is designed.The multi-scale extraction is realized by three different dehazing sub-networks:hue H,saturation S and intensity I,and the mapping relationship between hazy image and clear image is obtained by deep learning.Finally,the model was trained and tested with hazy data set.The experimental results show that this method can achieve good dehazing effect for both synthetic hazy images and real hazy images,and is superior to other contrast algorithms in subjective and objective evaluations.
