Brightness Perceiving for Recursive Low-Light Image Enhancement

2024-06-01

IEEE Transactions on Artificial Intelligence

AI Summary

To address the severe contrast degradation and heterogeneous detail loss caused by wide dynamic range in real-world low-light scenes—challenging end-to-end enhancement methods to achieve unified improvement—this paper proposes a brightness-aware recursive enhancement framework. Methodologically, it introduces (1) a novel Brightness Perception Network (BP-Net) that dynamically determines and controls the number of recursive enhancement iterations; (2) a dual-branch Adaptive Contrast and Texture Network (ACT-Net) jointly optimizing luminance distribution and gradient-based texture fidelity; and (3) an unsupervised collaborative training strategy leveraging a self-constructed wide-luminance-distribution dataset for joint optimization. Quantitatively, the method achieves state-of-the-art performance across six reference-based and reference-free metrics, with a 0.9 dB PSNR gain. Qualitatively, it significantly improves detail preservation and visual naturalness in extremely dark and mid-to-low-light regions, demonstrating strong generalization capability.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Learning to Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Due to the wide dynamic range in real low-light scenes, there will be large differences in the degree of contrast degradation and detail blurring of captured images, making it difficult for existing end-to-end methods to enhance low-light images to normal exposure. To address the above issue, we decompose low-light image enhancement (LLIE) into a recursive enhancement task and propose a brightness perceiving-based recursive enhancement framework for high dynamic range LLIE. Specifically, our recursive enhancement framework consists of two parallel subnetworks: adaptive contrast and texture enhancement network (ACT-Net) and brightness perception network (BP-Net). The ACT-Net is proposed to adaptively enhance image contrast and details under the guidance of the brightness adjustment branch and gradient adjustment branch, which are proposed to perceive the degradation degree of contrast and details in low-light images. To adaptively enhance images captured under different brightness levels, BP-Net is proposed to control the recursive enhancement times of ACT-Net by exploring the image brightness distribution properties. Finally, in order to coordinate ACT-Net and BP-Net, we design a novel unsupervised training strategy to facilitate the training procedure. To further validate the effectiveness of the proposed method, we construct a new dataset with a broader brightness distribution by mixing three low-light datasets. Compared with eleven existing representative methods, the proposed method achieves new state-of-the-art (SOTA) performance on six reference and no-reference metrics. Specifically, the proposed method improves the peak signal-to-noise ratio (PSNR) by 0.9 dB compared to the existing SOTA method.

Problem

• Enhancing low-light images with wide dynamic range
• Adaptive contrast and texture enhancement for degraded images
• Unsupervised training for recursive brightness perception

Innovation

• Recursive enhancement framework with parallel sub-networks
• Brightness perception controls adaptive enhancement
• Unsupervised training strategy for network coordination