Low-light Image Enhancement Algorithm Based on Exposure Prediction and Hybrid Feature Weighted Fusion Strategy

Abstract

Abstract First, based on a given low-light image, moderately exposed and overexposed images are generated through the Exposure Image Prediction Model (EIPM).Then all three generate feature maps by a Feature Extraction Module (FEM),and then the feature maps of them are denoised. Subsequently, the feature map of the low-light image is fused with the feature maps of the two exposure rates images respectively. Finally, an adaptive weighted fusion method is used to obtain ultimate enhanced image. Experiments show that by comparing with the current SOTA algorithms,the proposed ELLIE algorithm, both in the detail feature retention and in color recovery have been significantly improved, and the subjective and objective evaluation indicators also have obvious advantages.