Single-Image High Dynamic Range Reconstruction via Improved HDRUNet with Attention and Multi-Component Loss

High dynamic range (HDR) imaging aims to overcome the limited dynamic range of traditional imaging systems and achieve effective restoration of the brightness and color of the real world. In recent years, single-image HDR (SI-HDR) reconstruction technology has become a research hotspot due to its simple acquisition process and applicability to dynamic scenes. This paper proposes an improved SI-HDR reconstruction method based on HDRUNet, which systematically integrates channel, spatial attention mechanism, brightness expansion, and color-enhancement branches, and constructs an adaptive multi-component loss function. This effectively enhances the detail restoration in extreme exposure areas and improves the overall color expressiveness. Experiments on public datasets such as NTIRE 2021, VDS, and HDR-Eye show that the proposed method outperforms the mainstream SI-HDR methods in terms of PSNR, SSIM, and VDP evaluation metrics. It performs particularly well in complex scenarios, demonstrating greater robustness and generalization ability.