Providing an effective way to enhance low-light images: Enhanced Illumination Map Optimally

Low-light images often suffer from low brightness and contrast, which makes some scene details hard to see.This can affect the performance of many computer vision tasks, such as object recognition, tracking, scene understanding, and occlusion detection.Therefore, it is important and useful to enhance low-light images.One technique to enhance low-light images is based on the Retinex theory, which decomposes images into two components: reflection and illumination.Several mathematical models have been recently developed to estimate the illumination map using this theory.These methods first compute an initial illumination map and then refine it by solving a mathematical model. 2 60 92This paper introduces a novel method based on the Retinex theory to estimate the illumination map.The proposed method employs a new mathematical model with a differentiable objective function, unlike other similar models.This allows us to use more diverse methods to solve the proposed model, as classical optimization methods such as Newton, Gradient, and Trust-Region methods need the objective function to be differentiable.The proposed model also has linear constraints and is convex, which are desirable properties for optimization.We use the CPLEX solver to solve the proposed model, as it performs well and exploits the features of the model.Finally, we improve the illumination map obtained from the mathematical model using a simple linear transformation.This paper introduces a new method based on the Retinex theory for enhancing low-light images.The proposed method improves the illumination and the visibility of the scene details.We compare the performance of our method with six existing methods: AMSR, NPE, SRIE, DONG, MF, and LIME.We use four common metrics to evaluate the visual quality of the enhanced images: AMBE, LOE, SSIM, and NIQE.The results demonstrate that our method is competitive with many of the state-of-the-art methods for low-light image enhancement.