Structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated Images

Abstract

Images acquired in poor illumination conditions are characterized by low brightness and considerable noise which constrain the performance of computer vision systems. Image enhancement thus remains crucial for improving the efficiency of such systems. To improve the visibility of low-light images, a novel image enhancement framework based on the structure-texture decomposition is proposed in this paper. Firstly, the low-light image is split into structure and texture layers using the total-variation (TV) based image decomposition approach. The structure layer is initially diffused using Perona-Malik (PM) diffusion model and the local and global luminance enhancement is incorporated in the structure-pathway using an expanded model of biological normalization for visual adaptation. In the texture pathway, the suppression of local noise and the enhancement of image details are attained with the estimation of the local energy of the texture layer and the strategy of energy weighting. Eventually, the final enhanced image of improved quality is obtained by merging the modified structure and texture layers. The effectiveness of the proposed framework is validated using no-reference image quality metrics including IL-NIQE, BRISQUE, PIQE, and BLIINDS-II. The experimental results show that the proposed method outperforms the state-of-the-art approaches.