Single-Image High Dynamic Range Reconstruction Based on Multi-Attention and Perceptual Weighted Learning

KE De-zhang; CHEN Ye-yao; XU Hai-yong; JIN Chong-chong; JIANG Gang-yi

doi:10.12263/DZXB.20241055

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Single-Image High Dynamic Range Reconstruction Based on Multi-Attention and Perceptual Weighted Learning

PAPERS | 更新时间：2025-10-16

- Single-Image High Dynamic Range Reconstruction Based on Multi-Attention and Perceptual Weighted Learning
- ACTA ELECTRONICA SINICA Vol. 53, Issue 6, Pages: 2063-2078(2025)
- 作者机构：
  
  1.宁波大学信息科学与工程学院，浙江宁波 315211
  2.宁波大学数学与统计学院，浙江宁波 315211
  3.宁波大学科学技术学院，浙江宁波 315300
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62401301;62271276;62171243);Natural Science Foundation of Zhejiang Province(LQ24F010002)
- DOI：10.12263/DZXB.20241055
  CLC： TP391;
- Received：23 November 2024，
  
  Revised：2025-03-13，
  
  Published：25 June 2025
- 稿件说明：
移动端阅览
柯德涨, 陈晔曜, 徐海勇, 等. 基于多重注意力和感知加权学习的单图像高动态范围重建[J]. 电子学报, 2025, 53(06): 2063-2078.

KE De-zhang, CHEN Ye-yao, XU Hai-yong, et al. Single-Image High Dynamic Range Reconstruction Based on Multi-Attention and Perceptual Weighted Learning[J]. Acta Electronica Sinica, 2025, 53(06): 2063-2078.
柯德涨, 陈晔曜, 徐海勇, 等. 基于多重注意力和感知加权学习的单图像高动态范围重建[J]. 电子学报, 2025, 53(06): 2063-2078. DOI：10.12263/DZXB.20241055

KE De-zhang, CHEN Ye-yao, XU Hai-yong, et al. Single-Image High Dynamic Range Reconstruction Based on Multi-Attention and Perceptual Weighted Learning[J]. Acta Electronica Sinica, 2025, 53(06): 2063-2078. DOI：10.12263/DZXB.20241055

摘要

单图像高动态范围（High Dynamic Range，HDR）重建能够避免多曝光HDR成像可能造成的鬼影伪像，正受到广泛研究.然而，现有方法由于缺乏对重要信息的关注，尚不能很好地恢复曝光不良区域的细节信息.为解决该问题，本文提出了一种基于多重注意力和感知加权学习的单图像HDR重建方法，旨在从单幅低动态范围图像中推断出高保真的HDR图像.具体而言，考虑到恢复曝光不良区域需参考其他区域的补偿信息，本文设计了具有全局-局部感受野的多重注意力视觉Transformer （Multi-Attention Vision Transformer，MA-ViT），其将深度可分离卷积和注意力机制相结合，从而实现更有效的全局和局部特征提取与交互.此外，还提出了一种损失感知加权图以引导网络聚焦曝光不良区域，进一步提升HDR重建质量.本文在多个基准数据集上构建了全面的对比实验，结果表明所提出方法相较于目前最先进的方法在平均峰值信噪比（Peak Signal to Noise Ratio，PSNR）上提高了0.23 dB，同时生成了具有更高视觉质量的HDR重建结果.

Abstract

Single-image high dynamic range (HDR) reconstruction can avoid ghosting artifacts that may be caused by multi-exposure HDR imaging

and is receiving widespread research. However

existing methods still struggle to effectively restore detail information in poorly exposed regions due to a lack of focus on critical information. To address this issue

this paper proposes a single-image HDR reconstruction method based on multi-attention and perceptual weighted learning

which aims to infer a high-fidelity HDR image from a single low dynamic range (LDR) image. Specifically

considering that the restoration of poorly exposed regions requires reference to compensation information from other regions

a multi-attention vision transformer (MA-ViT) with global-local receptive fields is designed. It combines depthwise separable convolution and attention mechanisms to achieve more effective global and local feature extraction and interaction. In addition

a loss aware weighted map is proposed to guide the network to focus on poorly exposed regions

further enhancing the quality of HDR reconstruction. Comprehensive comparative experiments are conducted on multiple benchmark datasets

and the results show that the proposed method improves the average peak signal to noise ratio (PSNR) by 0.23 dB compared to the state-of-the-art method

while generating HDR reconstruction results with higher visual quality.

关键词

Keywords

references

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