Low-Light Image Enhancement Based on Illumination Reliability Mask
To address issues of local overexposure,local color cast,and additional noise in low-light image enhancement for scenarios with local strong light sources and colored light sources,a low-light image enhancement algorithm based on illumination reliability masks is proposed.The core idea is marking unreliable illumination regions such as ultra-low illumination,overexposure,and single-channel overexposure pixel-by-pixel according to the illumination components of the input image,followed with an"S-shaped"illumination reliability mask curve model which is established to eliminate local illumination inconsistencies in the input low-light image.Based on this,a low-light image enhancement network is designed,consisting of an illumination estimation sub-module,an illumination consistency correction sub-module,and a low-light image enhancement sub-module.Extensive experiments on test datasets such as MEF,LIME,DICM,and VV demonstrate that the proposed algorithm can significantly eliminate issues of local overexposure,local color cast,and additional noise,and outperforms existing algorithms in metrics such as NIQE(natural image quality evaluator)and SSIM(structural similarity index).The relevant code and pre-trained models can be accessed at https://github.com/fififft/MLightGAN.
万方数据
维普
