WEC-DG: Multi-Exposure Wavelet Correction Method Guided by Degradation Description

2025-08-13

AI Summary

Single-exposure images captured under complex illumination often suffer from brightness distortion and detail loss, while existing methods struggle to identify and correct the ambiguous “blurry” exposure degradation. To address this, we propose a degradation-description-guided multi-exposure correction framework operating in the wavelet domain. Our approach features two key innovations: (1) an Exposure Condition Attention Module (ECAM) that explicitly models exposure degradation types to ensure consistent cross-scene exposure alignment; and (2) a light-detail decoupled wavelet restoration mechanism—where low-frequency components optimize global exposure, and high-frequency priors guide local detail reconstruction. Extensive experiments on multiple public benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches in PSNR, SSIM, and perceptual quality, achieving superior brightness accuracy, enhanced contrast, and improved texture fidelity.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Multi-exposure correction technology is essential for restoring images affected by insufficient or excessive lighting, enhancing the visual experience by improving brightness, contrast, and detail richness. However, current multi-exposure correction methods often encounter challenges in addressing intra-class variability caused by diverse lighting conditions, shooting environments, and weather factors, particularly when processing images captured at a single exposure level. To enhance the adaptability of these models under complex imaging conditions, this paper proposes a Wavelet-based Exposure Correction method with Degradation Guidance (WEC-DG). Specifically, we introduce a degradation descriptor within the Exposure Consistency Alignment Module (ECAM) at both ends of the processing pipeline to ensure exposure consistency and achieve final alignment. This mechanism effectively addresses miscorrected exposure anomalies caused by existing methods' failure to recognize 'blurred' exposure degradation. Additionally, we investigate the light-detail decoupling properties of the wavelet transform to design the Exposure Restoration and Detail Reconstruction Module (EDRM), which processes low-frequency information related to exposure enhancement before utilizing high-frequency information as a prior guide for reconstructing spatial domain details. This serial processing strategy guarantees precise light correction and enhances detail recovery. Extensive experiments conducted on multiple public datasets demonstrate that the proposed method outperforms existing algorithms, achieving significant performance improvements and validating its effectiveness and practical applicability.

Problem

• Address intra-class variability in multi-exposure image correction
• Improve exposure consistency and alignment in degraded images
• Enhance detail recovery through wavelet-based light-detail decoupling

Innovation

• Wavelet-based exposure correction with degradation guidance
• Degradation descriptor ensures exposure consistency alignment
• Light-detail decoupling via wavelet transform enhances recovery