PROJECT TITLE :

Retinex-Based Perceptual Contrast Enhancement In Images Using Luminance Adaptation - 2017

ABSTRACT:

In this paper, we propose retinex-based mostly perceptual distinction enhancement in pictures using luminance adaptation. We have a tendency to use the retinex theory to decompose an image into illumination and reflectance layers, and adopt luminance adaptation to handle the illumination layer that causes detail loss. First, we obtain the illumination layer using adaptive Gaussian filtering to get rid of halo artifacts. Then, we have a tendency to adaptively remove illumination of the illumination layer in the multi-scale retinex (MSR) process based on luminance adaptation to preserve details. Finally, we perform contrast enhancement on the MSR result. Experimental results demonstrate that the proposed method successfully enhances distinction in images while keeping textures in highlight regions.