Low Light Image Enhancement Based on Retinex Theory and Diffusion Model

Abstract

This article proposes a new method Maximum Decomposition Diffusion Enhancement(MDDE) for low light image enhancement. This method combines the advantages of Retinex theory and diffusion models, making the model physically interpretable and improving the contrast of low light images, making the images clearer. This method is divided into two modules: Maximum Value Decomposition Module and Diffusion Enhancement Module. The Maximum Value Decomposition Module is mainly based on Retinex theory and decomposes the image into illumination and reflection maps based on the maximum value of the three channels of the image. The Diffusion Enhancement Module is divided into two modules: Enhanced Illumination Diffusion Module and Final Enhanced Diffusion Module. This module mainly implements the image processing of low light images based on the diffusion probability model. The experiment shows that our method produces clearer images compared to other methods.