Edge and Color Information Guided High-resolution Low-light Image Enhancement Algorithm

The ability of the device to capture high-resolution images poses a new challenge to image processing,and most of the existing low-light image enhancement algorithms are designed for low-resolution images,and there are problems such as unclear details and color distortion when dealing with high-resolution images.Using the texture information and color information contained in the image itself,an edge and color information guided high-resolution low-light image enhancement algorithm is proposed.To improve the limitation of local feature learning of convolutional neural network,an edge decoder is introduced,which helps to capture the key information in the image at a long distance and improves the encoding of semantic information at the boundary.In addition,in order to deal with high-resolution images,a sparse attention mechanism is introduced in the context attention blocks,which focuses on the important information in the image and effectively reduces noise interference.On the other hand,the color decoder effectively utilizes the chromaticity cues of the low-light image itself to improve the accuracy of color information recovery.