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Two difficulties here make low-light image enhancement a challenging task;firstly, it needs to consider not only luminance restoration but also imagecontrast, image denoising and color distortion issues simultaneously. Second,the effectiveness of existing low-light enhancement methods depends on pairedor unpaired training data with poor generalization performance.  To solve these difficult problems, we propose in this paper a newlearning-based Retinex decomposition of zero-shot low-light enhancement method,called ZERRINNet. To this end, we first designed the N-Net network, togetherwith the noise loss term, to be used for denoising the original low-light imageby estimating the noise of the low-light image. Moreover, RI-Net is used toestimate the reflection component and illumination component, and in order tosolve the color distortion and contrast, we use the texture loss term andsegmented smoothing loss to constrain the reflection component and illuminationcomponent. Finally, our method is a zero-reference enhancement method that isnot affected by the training data of paired and unpaired datasets, so ourgeneralization performance is greatly improved, and in the paper, we haveeffectively validated it with a homemade real-life low-light dataset andadditionally with advanced vision tasks, such as face detection, targetrecognition, and instance segmentation. We conducted comparative experiments ona large number of public datasets and the results show that the performance ofour method is competitive compared to the current state-of-the-art methods. Thecode is available at:https://github.com/liwenchao0615/ZERRINNet

Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition