GAN Architecture Leveraging a Retinex Model With Colored Illumination for Low-Light Image Restoration

Abstract

In this work, we study the restoration of text low-light images with outdoor scenes without ground truth. Until now, approaches in the literature have avoided using the Retinex decomposition model in an unsupervised way or have added constraining priors on the searched components. We propose here to relax the constraint of a grayscale illumination of the Retinex model. Indeed, according to the physics of light, it should include a colored illumination. Resulting from this new decomposition model, we formulate a new deep learning-based architecture inspired by the style transfer methods. Our method enables us to visualize the illumination (i.e. a complex style with the same dimensions as an image) and the reflectance (i.e. the content). It achieves more visually pleasing components compared to the state-of-the-art i.e. without artifact, without noise amplification and without hallucination with a simple restoration for each of the components.

Keywords

• Low light enhancement
• image decomposition
• image restoration
• inverse problems
• Retinex model
• neural networks