Low -light image enhancement based on dual -residual convolutional network

In order to solve the current problem about low-light image enhancement, an algorithm of image enhancement based on dual -residual convolutional network is proposed. First, according to Retinex theory, the normal -light image is synthesized into low-light image, the synthetic is decomposed onto the three components of R,G and B,and learning the mapping relations between low-light image and normal -light image on all components through the module of feature extraction as well as dual-residual. Then the enhanced image on all components can be obtained, and finally the enhanced RGB image is synthesized. Subsequently, the bilateral filtering is used to optimize the enhanced RGB image so that the obtained image is analogical to the reference image.The experiment results show that the algorithm proposed in this paper, whose Peak Signal to Noise Ratio can reach up to 25.931 1 dB and Structural Similarity Index can reach up to 0.945 2 in terms of processing synthesized low-light image, and whose novel blind image quality assessment can exceed other compared algorithms and the algorithm in this paper goes faster in terms of processing real low-light image.Therefore,the proposed al-gorithm is superior to the contrast algorithms.