$$l_{1/2}$$ regularized joint low rank and sparse recovery technique for illumination map estimation in low light image enhancement

Insufficient illumination causes poor discernibility of visuals captured under low light environment. Often, computer vision algorithms designed to process visuals taken under normal light struggle to handle visuals with low visibility. The present work proposes a low rank approximation (LRA) based optimization model in producing the brightness enhanced version from the input low light image. The work adopts $$l_{1/2}$$ nuclear norm and sparsity minimization combined with total variation (TV) regularization to estimate the illumination map required to reconstruct the clear image. The $$l_{1/2}$$ nuclear norm serves primarily for smoothing the coarse illumination map, while TV regularization helps to preserve the prominent edge information. Additionally, $$l_{1/2}$$ regularization aids to retain the sparse structural details. The intelligence of the proposed method is bounded in the efficient formulation of a unified optimization model for the estimation of illumination map with low rank and sparsity concepts which is never reported in the direction of low light image enhancement. Furthermore, extensive experiments confirm that the proposed method outperforms the current state of art methods in low light image enhancement.