App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 10

Spatiotemporal cross-attention Mamba network for Quad-Bayer color video snapshot compressive imaging reconstruction

Guo Xiaotao1
Zhang Pengwei1
Chen Jingxia1
Ji Chao2
1. School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi'an 710021, China
2. Engineering Research Center of Streak Camera, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
DOI: 10.19734/j.issn.1001-3695.2026.01.0053

Abstract

This study investigates the reconstruction problem in Quad-Bayer color video snapshot compressive imaging (Snapshot Compressive Imaging, SCI) , where color distortion, mosaic artifacts, and insufficient spatiotemporal detail recovery occur. An efficient spatiotemporal U-Net reconstruction framework, termed QBMamba-Unet, is constructed for Quad-Bayer measurements. The framework adopts an asymmetric U-Net architecture and introduces a residual-enhanced feature extraction pathway based on RLC-Mamba in the encoding stage. It utilizes a Hilbert-curve-based scanning strategy within QLC-Mamba to reorganize spatiotemporal sequences, thereby improving local structure preservation and long-range dependency modeling. An Enhancement Feature Module (Enhancement Feature Module, EFM) is incorporated to strengthen channel interaction and semantic fusion. A lightweight cross-attention skip fusion mechanism (Lightweight Cross-Attention skip fusion, LCA skip fusion) is designed between the encoder and decoder to achieve effective alignment and fusion of multi-scale features. Experimental results on simulated color video datasets demonstrate that the method improves color restoration, detail preservation, and reconstruction stability. The peak signal-to-noise ratio (PSNR) reaches 36.58 dB, and the structural similarity index (SSIM) reaches 0.96, indicating improved overall performance compared with existing methods.

Foundation Support

中子科学与技术全国重点实验室基金资助项目(NST20240105)
国家自然科学基金资助项目(12105360)
中国科学院西安光机所光子项目(E35118Z6)
中国科学院科研仪器设备研制项目(PTYQ2024TD0028)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.01.0053
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 10

Publish History

[2026-07-29] Accepted Paper

Cite This Article

郭晓涛, 张鹏伟, 陈景霞, 等. 面向Quad-Bayer彩色视频快照压缩成像重建的时空交叉注意力Mamba网络 [J]. 计算机应用研究, 2026, 43 (10). (2026-07-30). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0053. (Guo Xiaotao, Zhang Pengwei, Chen Jingxia, et al. Spatiotemporal cross-attention Mamba network for Quad-Bayer color video snapshot compressive imaging reconstruction [J]. Application Research of Computers, 2026, 43 (10). (2026-07-30). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0053. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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