Two-Dimensional Compact Variational Mode Decomposition-Based Low-Light Image Enhancement

Abstract

In this paper, a novel methodology is proposed for low-light image enhancement. The proposed algorithm contains three stages: image reconstruction, image enhancement and color restoration. Two-dimensional compact variational mode decomposition (2D-TV-VMD) is employed to covert the RGB image into gray map through decomposing it on multiple gray eigenfunctions. A binary artifact indicator function is used to identify and eliminate potential artifact pixels in an image, and then low-light image enhancement via illumination map estimation (LIME) is used to enhance the reconstructed gray-scale map. Finally, color restoration is performed in RGB-color space to recover the color information. Subjective evaluation and objective evaluation of the proposed method, including no-reference image quality metric of contrast-distorted images based on information maximization (NIQMC), is conducted on different low-light images. Objective and subjective experimental performance demonstrate the competitive performance of the proposed algorithm compared with other state-of-art methods.