Attention-Edge-Assisted Neural HDRI Based on Registered Extreme-Exposure-Ratio Images

In order to improve image visual quality in high dynamic range (HDR) scenes while avoiding motion ghosting artifacts caused by exposure time differences, innovative image sensors captured two registered extreme-exposure-ratio (EER) image pairs with complementary and symmetric exposure configurations for high dynamic range imaging (HDRI). However, existing multi-exposure fusion (MEF) algorithms suffer from luminance inversion artifacts in overexposed and underexposed regions when directly combining such EER image pairs. This paper proposes a neural network-based framework for HDRI based on attention mechanisms and edge assistance to recover missing luminance information. The framework derives local luminance representations from a convolution kernel perspective, and subsequently refines the global luminance order in the fused image using a Transformer-based residual group. To support the two-stage process, multi-scale channel features are extracted from a double-attention mechanism, while edge cues are incorporated to enhance detail preservation in both highlight and shadow regions. The experimental results validate that the proposed framework can alleviate luminance inversion in HDRI when inputs are two EER images, and maintain fine structural details in complex HDR scenes.