基于多分支融合复数网络的全息图像失真矫正

陈逸飞; 刘延伟; 刘金霞; 古晓艳

doi:10.12263/DZXB.20250549

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基于多分支融合复数网络的全息图像失真矫正

学术论文 | 更新时间：2025-12-27

- 基于多分支融合复数网络的全息图像失真矫正
- Holographic Image Distortion Correction Based on Multi-Branch-Fused Complex-Valued Neural Network
- 电子学报 2025年53卷第9期页码：3319-3330
- 作者机构：
  
  1.中国科学院信息工程研究所，北京 100085
  2.中国科学院大学网络空间安全学院，北京 101408
  3.网络空间安全防御全国重点实验室，北京 100085
  4.浙江万里学院，浙江宁波 315100
- 作者简介：
  
  [ "陈逸飞男，2000年出生于福建省泉州市.现为华为技术有限公司工程师.主要研究方向为计算机生成全息图像的理论及应用.E-mail: cyf_361@163.com" ]
  [ "刘延伟男，1976年出生于黑龙江省克山县.现为中国科学院信息工程研究所副研究员.主要研究方向为全息通信、智能多媒体信息处理、人工智能安全与数据安全.E-mail: liuyanwei@iie.ac.cn" ]
  [ "刘金霞女，1969年出生于黑龙江省齐齐哈尔市.现为浙江万里学院教授.主要研究方向为沉浸式通信与多媒体通信.E-mail: liujinxia1969@126.com" ]
  [ "古晓艳女，1987年出生于山东省济宁市.现为中国科学院信息工程研究所正高级工程师.主要研究方向为数据安全、人工智能安全、深度学习.E-mail: guxiaoyan@iie.ac.cn" ]
- 基金信息：
  
  国家自然科学基金(62371450)
- DOI：10.12263/DZXB.20250549
  中图分类号： TP391.41;TP18
- 收稿：2025-06-24，
  
  录用：2025-09-05，
  
  纸质出版：2025-09-25
- 稿件说明：
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陈逸飞, 刘延伟, 刘金霞, 等. 基于多分支融合复数网络的全息图像失真矫正[J]. 电子学报, 2025, 53(09): 3319-3330.

CHEN Yi-fei, LIU Yan-wei, LIU Jin-xia, et al. Holographic Image Distortion Correction Based on Multi-Branch-Fused Complex-Valued Neural Network[J]. Acta Electronica Sinica, 2025, 53(09): 3319-3330.
陈逸飞, 刘延伟, 刘金霞, 等. 基于多分支融合复数网络的全息图像失真矫正[J]. 电子学报, 2025, 53(09): 3319-3330. DOI：10.12263/DZXB.20250549

CHEN Yi-fei, LIU Yan-wei, LIU Jin-xia, et al. Holographic Image Distortion Correction Based on Multi-Branch-Fused Complex-Valued Neural Network[J]. Acta Electronica Sinica, 2025, 53(09): 3319-3330. DOI：10.12263/DZXB.20250549

摘要

全息显示技术可以再现出包含物体全部信息的三维成像，能为用户带来高度逼真的视觉体验，被认为是目前最理想的裸眼三维显示技术.全息显示带来的独特三维沉浸式体验使得全息通信在医疗、教育、虚拟现实等领域具有广泛的应用前景.但全息通信技术的大规模商业化应用还面临众多问题.其中，影响全息通信质量的一个主要问题就是在通信过程中压缩噪声与信道干扰等导致的多重混叠失真.现有图像失真矫正技术大多聚焦单一失真类型，难以应对复杂场景下的全息混合失真问题，严重制约全息技术的实际应用效果.针对这一难题，本文提出一种基于多分支复数注意力网络的全息图像失真矫正方法，通过构建分层并行的多分支网络结构，实现对全息图像多尺度、多维度特征的深度提取与协同融合；同时提出复数域自适应注意力机制，强化网络对相位畸变、振幅衰减等关键失真特征的感知与抑制能力，从而实现对压缩、传输等全链路失真的精准矫正.在包含压缩和信道噪声等混合类型的全息失真矫正实验中，相较于现有先进的深度学习失真矫正方法SCUNet（Swin-Conv-UNet），本文方法在峰值信噪比指标上提升0.41 dB以上，结构相似性指标提升0.006以上，有效抑制了振幅失真导致的亮度异常，矫正了相位畸变，显著提升了全息图像的重建质量.

Abstract

Holographic display technology can reproduce three-dimensional imaging that encompasses all information of an object

providing users with a highly realistic visual experience. It is regarded as the most perfect naked-eye 3D display technology currently available. The unique immersive 3D experience offered by holographic displays gives holographic communication has broad application prospects in fields such as healthcare

education

and virtual reality. However

the large-scale commercial application of holographic communication technology still currently confronts numerous obstacles. Among them

one major issue affecting the quality of holographic communication is the multiple aliasing distortions caused by compression noise and channel interference during hologram transmission. Existing image distortion correction techniques mostly focus on single distortion type and struggle to address the problem of mixed holographic distortions in complex scenarios

severely limiting the effectiveness of holographic technology in the practical applications. To tackle this issue

this paper proposes a holographic image distortion correction method based on a multi-branch complex-valued convolutional neural network. This method constructs a multi-level parallel multi-branch network architecture to achieve in-depth extraction and collaborative fusion of multi-scale and multi-dimensional distortion features of holographic images. Simultaneously

a complex-valued adaptive attention mechanism is proposed to enhance the network’s perception and suppression capabilities for key distortion features such as phase distortion and amplitude attenuation

thereby achieving precise correction of end-to-end distortions caused during compression and transmission. In the experiments involving mixed-type holographic distortions including compression and channel noises

compared to the state-of-the-art deep learning distortion correction method SCUNet (Swin-Conv-UNet)

the proposed method achieves an average improvement of over 0.41 dB in peak signal-to-noise ratio (PSNR) and an average increase of over 0.006 in structural similarity index (SSIM). These experimental results show that the proposed method can effectively suppress the brightness abnormalities caused by amplitude distortion

correct the phase distortions

and significantly enhance the reconstruction quality of holographic images.

关键词

Keywords

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