Self-supervised Image Enhancement Network: Training with Low Light Images Only

Abstract

This paper proposes a self-supervised low light image enhancement method based on deep learning. Inspired by information entropy theory and Retinex model, we proposed a maximum entropy based Retinex model. With this model, a very simple network can separate the illumination and reflectance, and the network can be trained with low light images only. We introduce a constraint that the maximum channel of the reflectance conforms to the maximum channel of the low light image and its entropy should be largest in our model to achieve self-supervised learning. Our model is very simple and does not rely on any well-designed data set (even one low light image can complete the training). The network only needs minute-level training to achieve image enhancement. It can be proved through experiments that the proposed method has reached the state-of-the-art in terms of processing speed and effect.

14 pages,13 figures

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Cited by in corpus (11)

• A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement
• Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement
• Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement
• Brightness Perceiving for Recursive Low-Light Image Enhancement
• A review of advancements in low-light image enhancement using deep learning
• Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image Enhancement
• DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement
• BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration
• A Two-stage Unsupervised Approach for Low light Image Enhancement
• Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement
• Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks