Low-light image enhancement algorithms have been introduced to improve the visual quality of low-light images that may degrade the performance of many computer vision and multimedia systems designed for high-quality images. However, the existing bright channel prior and maximum colour channel enhancement algorithms introduce halo artifacts and colour distortions while enhancing the images. To overcome these limitations, in this paper, an effective fusion-based low-light image enhancement algorithm is proposed. In the proposed algorithm, the illumination of the low-light image is estimated from both the bright and maximum colour channels to overcome the halo artifacts and colour distortion problems. Further, an effective refinement method is utilized to improve the sharpness of the initial enhanced image representing the scene reflectance. Experiment results show that the proposed algorithm outperforms the state-of-the-art algorithms qualitatively and quantitatively. Moreover, the proposed algorithm reduces the halo artifacts and colour distortion and enhances the details while preserving the naturalness.How to cite this article: Sandoub G, Atta R, Ali HA, Abdel-Kader RF. A low-light image enhancement method based on bright channel prior and maximum colour channel.

The images taken in low-light conditions often have many flaws such as, color vividness and low visibility which negatively affects the performance of many vision-based systems. Many of the existing Retinex-based enhancement algorithms improve the visibility of low-light images via estimating the illumination map and use it to obtain the corresponding reflectance. However, the improper estimation of the initial illumination map may produce unsatisfactory illuminated enhanced images with weak color constancy. To address this problem, this paper proposes an efficient algorithm for the enhancement of low-light images. In this algorithm, the initial illumination map is obtained by the fusion between the maximum color channel and bright channel prior. The estimated initial illumination map is then refined using a multi-objective problem that contains the illumination regularization terms specifically, the structural and textural details of the illumination. The optimization problem is solved using the alternative direction minimization (ADM) technique with the augmented Lagrangian multiplier to produce structure-aware smoothness of the initial illumination map. Finally, the contrast of the refined illumination map is adjusted using the gamma correction method. Experimental results on several benchmark datasets reveal the superiority of the proposed algorithm on the state-of-theart algorithms in terms of qualitative and quantitative analysis. Furthermore, the proposed algorithm produces enhanced images with reducing the artifacts and preserving the naturalness and structural details.