App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Multimodal physical-attribute-guided cascaded reconstruction for fMRI visual decoding

Gan Jianfeng
Chen Jiajia
Liu Yingyuan
Zhao Xuerong
College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China
DOI: 10.19734/j.issn.1001-3695.2026.02.0080

Abstract

Functional magnetic resonance imaging (fMRI) visual decoding frequently faces the challenge of semantic-perceptual imbalance, in which high-level semantic consistency often compromises low-level visual fidelity, especially under the low signal-to-noise ratio inherent in single-subject fMRI data. This study proposed a multimodal physical-attribute-guided cascaded reconstruction (MGCR) method for fMRI visual decoding. The MGCR method introduces an adaptive sparse voxel selection mechanism to suppress non-visual noise in fMRI signals and improve the stability of perceptual structure decoding. Furthermore, it incorporates explicit color and depth estimation modules to impose physical attributes of visual stimuli as strong constraints, thereby enhance geometric structure and color consistency. Based on these mechanisms, the method employs a coarse-to-fine cascaded reconstruction strategy to progressively integrate multimodal information during image generation. Experimental results on the Natural Scenes Dataset demonstrate that the proposed method outperforms existing approaches in terms of pixel-level correlation, structural similarity, color and depth fidelity. These results indicate that multimodal physical-attribute guidance effectively enhances perceptual reconstruction quality in fMRI visual decoding.

Foundation Support

国家自然科学基金青年项目(62006172)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.02.0080
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-09] Accepted Paper

Cite This Article

淦剑锋, 陈佳佳, 刘迎圆, 等. 面向fMRI视觉解码的多模态物理属性引导级联重建方法 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0080. (Gan Jianfeng, Chen Jiajia, Liu Yingyuan, et al. Multimodal physical-attribute-guided cascaded reconstruction for fMRI visual decoding [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0080. )

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.

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