MSR-net:Low-light Image Enhancement Using Deep Convolutional Network

Abstract

Images captured in low-light conditions usually suffer from very low contrast, which increases the difficulty of subsequent computer vision tasks in a great extent. In this paper, a low-light image enhancement model based on convolutional neural network and Retinex theory is proposed. Firstly, we show that multi-scale Retinex is equivalent to a feedforward convolutional neural network with different Gaussian convolution kernels. Motivated by this fact, we consider a Convolutional Neural Network(MSR-net) that directly learns an end-to-end mapping between dark and bright images. Different fundamentally from existing approaches, low-light image enhancement in this paper is regarded as a machine learning problem. In this model, most of the parameters are optimized by back-propagation, while the parameters of traditional models depend on the artificial setting. Experiments on a number of challenging images reveal the advantages of our method in comparison with other state-of-the-art methods from the qualitative and quantitative perspective.

9pages

Cited by in corpus (8)

• Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network
• Self-supervised Low Light Image Enhancement and Denoising
• Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement
• Semantic Image Completion and Enhancement using Deep Learning
• DALE : Dark Region-Aware Low-light Image Enhancement
• Extreme Low-Light Imaging with Multi-granulation Cooperative Networks
• Attention-based network for low-light image enhancement
• Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks