WEC-DG: Multi-Exposure Wavelet Correction Method Guided by Degradation Description
This work improves image restoration for visual applications by enhancing adaptability to complex lighting conditions, though it appears incremental with a novel hybrid method.
The paper tackles the problem of multi-exposure correction in images affected by poor lighting, proposing WEC-DG to address intra-class variability and exposure degradation issues, achieving significant performance improvements over existing algorithms on multiple datasets.
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.