SPLIE: Optimal Illumination Estimation for Structure Preserving Low-light Image Enhancement | ||||
| Port-Said Engineering Research Journal | ||||
| Article 12, Volume 25, Issue 2, September 2021, Page 120-134 PDF (1.72 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/pserj.2021.69512.1104 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ghada Sandoub   1; Randa Atta1; Rabab Farouk Abdel-Kader2; Hesham Arafat Ali3
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| 1Department of Electrical Engineering, Faculty of Engineering, Port Said University, Port Said, Egypt | ||||
| 2Department of Electrical Engineering, Faculty of Engineering, Port Said University, Port Said, Egypt | ||||
| 3Department of Computer Engineering and Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt | ||||
| Abstract | ||||
| 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-the-art 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. | ||||
| Keywords | ||||
| Image enhancement; Low-light image; Illumination estimation; Optimization | ||||
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