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Low-light image enhancement (LLIE) aims to improve the illuminance of imagesdue to insufficient light exposure. Recently, various lightweightlearning-based LLIE methods have been proposed to handle the challenges ofunfavorable prevailing low contrast, low brightness, etc. In this paper, wehave streamlined the architecture of the network to the utmost degree. Byutilizing the effective structural re-parameterization technique, a singleconvolutional layer model (SCLM) is proposed that provides global low-lightenhancement as the coarsely enhanced results. In addition, we introduce a localadaptation module that learns a set of shared parameters to accomplish localillumination correction to address the issue of varied exposure levels indifferent image regions. Experimental results demonstrate that the proposedmethod performs favorably against the state-of-the-art LLIE methods in bothobjective metrics and subjective visual effects. Additionally, our method hasfewer parameters and lower inference complexity compared to otherlearning-based schemes.

Learning a Single Convolutional Layer Model for Low Light Image Enhancement