Dynamic Exposure-Adaptive Learning for Multi-Exposure Image Fusion Using RAW-Derived Training Pairs

Abstract

Multi-exposure image fusion aims to expand the dynamic range of images by combining complementary information from differently exposed inputs. However, existing high dynamic range (HDR) reconstruction and exposure fusion methods often depend on HDR ground truth, tone mapping, or aligned multi-exposure data. To address these limitations, this study proposes a self-supervised high-exposure (HE) and low-exposure (LE) fusion framework for dynamic range expansion without requiring HDR ground truth. First, RAW-based exposure-range sampling augmentation generates diverse HE-LE training pairs from a single RAW image, expanding the training dataset while providing pixel-level aligned pairs without multi-shot acquisition. Second, a dynamic adaptive fusion module exploits complementary exposure information and balances local detail information with global structural information according to regional exposure characteristics. Fusion quality is progressively improved through Structural Similarity Index Measure (SSIM)-based coarse training and a stage-wise loss optimization strategy. Third, during inference, HDR-like enhancement and color compensation are applied to the LE image before fusion with the HE image to improve luminance, color consistency, and structural detail. Experimental results demonstrate that the proposed framework achieves stable dynamic range expansion without HDR ground truth and outperforms existing methods in structural preservation and visual quality, achieving the lowest BRISQUE (21.214), PIQE (33.979), and SSEQ (20.367) scores, as well as the highest MANIQA score (0.528), among all compared methods.