Due to different lighting environments and equipment limitations, low-light images have high noise, low contrast and unobvious colours. The main purpose of low-light image enhancement is to preserve the details and suppress noise as much as possible while improving the contrast of the image. Here, different networks are first combined to construct a multi-branch module for features extraction, and use the module and Retinex theory to extract the reflection map of the image. Then an attention mechanism is introduced into the multi-branch construction to balance the feature weight of each branch, and get the final result by the reconstruction module. The Retinex theory is used to calculate the L 1 loss and the gradient loss for the intermediate feature map of the entire model to train our framework. The entire process is completed in an end-to-end-way, which avoids the handcrafted reconstruction rules and reduces the workload. What's more, a large number of experiments demonstrate that the proposed framework performs better results than stateof-the-art algorithms in both quantitative and qualitative evaluations of image enhancement.

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