Single-Image HDR Reconstruction with Attention-Driven Autoencoder

Authors : Cevher Renk, Serdar Çiftçi

Pages : 45-54

View : 81 | Download : 68

Publication Date : 2025-09-29

Article Type : Research Paper

Abstract :High Dynamic Range (HDR) imaging enables the representation of details in both bright and dark areas of a scene, aligning closely with human visual perception. Traditional multi-exposure HDR methods face challenges such as ghosting, hardware dependency, and high processing costs. This study adopts a baseline model that synthesizes multi-exposure representations from a single SDR image using a U-Net-like autoencoder, which is enhanced by integrating five distinct attention mechanisms: Spatial, Channel, Bottleneck, Squeeze-and-Excitation and Self Attention. Each attention module is individually embedded into the encoder and decoder layers to form separate model variants, all trained independently on the DrTMO dataset. Quantitative evaluations based on SSIM, PSNR, and LPIPS demonstrate that the Spatial Attention variant delivers the best performance across all metrics. The results highlight that incorporating attention mechanisms into autoencoder-based HDR reconstruction architectures significantly enhances both structural fidelity and perceptual image quality, making them promising for efficient single-image HDR synthesis.
Keywords : YDA Yeniden Yapılandırma, Otokodlayıcı, Dikkat Mekanizmaları

ORIGINAL ARTICLE URL