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View-Aligned Nonlocal Low-Rank Tensor Reconstruction for Snapshot Compressive Multi-View Spectral Imaging System
Xiaorui Yin1, Lijuan Su1, Yu Wang1
1Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, Beihang University, Beijing 100191, China.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
This study introduces a novel reconstruction method for snapshot compressive multi-view spectral imaging (SC-MVSI). The technique improves image alignment and accuracy for spectral imaging applications.
Area of Science:
- Optics and Photonics
- Image Processing
- Computational Imaging
Background:
- Snapshot compressive multi-view spectral imaging (SC-MVSI) integrates spectral and view information into a single measurement for compact data acquisition.
- Direct reconstruction in SC-MVSI faces challenges due to view-dependent displacement, causing structural mismatches between spectral channels.
- Existing methods struggle to accurately align spatially varying information across different spectral and view channels.
Purpose of the Study:
- To develop an advanced reconstruction method for SC-MVSI that addresses structural mismatches caused by view-dependent displacement.
- To enhance the accuracy and quality of reconstructed multi-view spectral images.
- To enable more precise analysis of spectral and spatial information in compressed sensing imaging.
Main Methods:
- A reference-guided view-aligned nonlocal low-rank tensor reconstruction method is proposed.
- The reconstruction is formulated as a coded inverse problem and solved using the alternating direction method of multipliers (ADMM).
- A variable-splitting framework incorporates a reference tensor for block-level patch alignment and nonlocal tensor grouping, regularized by canonical polyadic (CP) low-rank approximation.
Main Results:
- The proposed method achieved the highest average Peak Signal-to-Noise Ratio (PSNR) of 33.61 dB and the lowest average Color Angle Error (CAE) of 5.69 degrees in synthesized multispectral light-field scenes.
- It obtained the second-highest average Structural Similarity Index Measure (SSIM) of 0.8823, indicating high fidelity reconstruction.
- Real-system experiments validated the framework's effectiveness on captured coded measurements.
Conclusions:
- The reference-guided view-aligned nonlocal low-rank tensor reconstruction method significantly improves SC-MVSI performance.
- The ADMM-based approach effectively handles view-dependent displacements, leading to superior image reconstruction quality.
- This work provides a robust framework for accurate multi-view spectral image reconstruction from compressed measurements.
