Fast and robust low-light image enhancement based on iterative propagation network
Low-light images suffer from low contrast and low signal-to-noise ratio,and the low quality of captured images seriously affects the subsequent observation and measurement.To enhance the quality of low-light images in complex application scenarios,this paper designs an iterative propagation network to accomplish the image enhancement task of micro-optical images in a fast and robust manner.Firstly,this work designs a multi-stage prediction model to model the illumination prediction task incrementally and enhances the nonlinear fitting capability of the model,thus adapting to unknown realistic situations.Considering the inference burden caused by the multi-stage model,this work also constructs iterative loops based on the Retinex forward propagation model to ensure each stage of the multi-stage model converges to a similar or even the same state to optimize the inference process,and significantly improves the inference speed while enhancing the model performance.This work conducts comparative experiments based on publicly available datasets,the average values of peak signal-to-noise ratio and structural similarity are 11.8%and 3.5%higher than the previous best comparison algorithms,respectively.The iterative propagation network is also used to enhance real-world low-exposure photographic images and low-excitation fluorescence microscopy images,and the experimental results demonstrate its excellent image enhancement performance and generalization.
neural networklow-light image enhancementcascaded structureretinex model
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