Low-light image enhancement method and system based on binary neural network

Abstract

The invention discloses a low-light image enhancement method and system based on a binary neural network, and relates to the technical field of low-light image enhancement, and the method comprises the steps: obtaining a full-precision network and a low-light image; a BiLLIE framework is adopted, on the basis of a full-precision network, a binarization module is used for gradually replacing a standard convolution module, and a full-precision dark light enhancement network is constructed; and inputting the low-light image into the full-precision dark light enhancement network to obtain an enhanced low-light image. According to the method, through binarization operation, the memory and the calculation amount are remarkably compressed, and the utilization efficiency of hardware resources is maximized. Through a two-step training strategy, smooth transition from a full-precision model to a binary model is realized. The full-precision weight is kept through activation value binaryzation, and the training stability is ensured and the precision loss is avoided through step-by-step binaryzation of the convolution weight. According to the training strategy, the model can gradually adapt to binarization operation, and the convergence of the training process and the final image enhancement performance are greatly improved.