Effective method for low-light image enhancement based on the JND and OCTM models

Abstract

Low-light images always suffer from dim overall brightness, low contrast, and low dynamic ranges, thus result in image degradation. In this paper, we propose an effective method for low-light image enhancement based on the just-noticeable-difference (JND) and the optimal contrast-tone mapping (OCTM) models. First, the guided filter decomposes the original images into base and detail images. After this filtering, detail images are processed based on the visual masking model to enhance details effectively. At the same time, the brightness of base images is adjusted based on the JND and OCTM models. Finally, we propose a new method to generate a sequence of artificial images to adjust the brightness of the output, which has a better performance in image detail preservation compared with other single-input algorithms. Experiments have demonstrated that the proposed method not only achieves low-light image enhancement, but also outperforms state-of-the-art methods qualitatively and quantitatively.