TCA-EfficientSCI: A Lightweight Causal Baseline for Cross-Measurement Temporal Continuity in Snapshot Compressive Imaging

Snapshot compressive imaging (SCI), including coded aperture compressive temporal imaging (CACTI), reconstructs high-speed video frames from compressed low-frame-rate measurements. Most deep SCI reconstruction networks are designed around a measurement-wise formulation: each compressed exposure is reconstructed independently, and the resulting frame segments are concatenated to form a continuous video. This protocol is effective for within-measurement reconstruction, but it leaves cross-measurement temporal continuity largely unmodeled. Boundary artifacts such as flickering, texture drift, or motion jumps can therefore appear between adjacent reconstructed segments, even when frame-wise reconstruction metrics remain competitive. This work identifies and empirically analyzes the underexplored problem of cross-measurement temporal continuity in continuous SCI, and it provides TCA-EfficientSCI as a lightweight, causal, and reproducible baseline. The Temporal Context Adapter uses the last m reconstructed frames from the previous measurement as causal temporal context and injects this history through a gated residual feature pathway. A boundary consistency loss regularizes the predicted temporal variation across measurement boundaries without forcing adjacent frames to be identical. In a controlled three-seed comparison, Full TCA with boundary loss reduces mean Boundary Difference Error (BDE) by 2.23% relative to the matched-epoch EfficientSCI control while maintaining similar PSNR and SSIM. Correct-history inference gives BDE 0.01615, while zero and shuffled history give 0.01725 and 0.01810, respectively. The adapter adds 1,019,905 parameters, or 11.56% relative to the EfficientSCI baseline parameters, and it changes 256×256 mean latency from 54.35 ms to 68.58 ms per measurement in the profiling protocol. Rather than claiming broad reconstruction-quality improvement, this study highlights cross-measurement continuity as an important evaluation and design dimension for continuous SCI deployment.

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Publication Details

Journal
Entropy
Published
2026-07-01
DOI
https://doi.org/10.3390/e28070742
Primary Topic
Sparse and Compressive Sensing Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

TCA-EfficientSCI: A Lightweight Causal Baseline for Cross-Measurement Temporal Continuity in Snapshot Compressive Imaging

Ziheng Cheng, Xing Liu, Mengyuan Liu, Xin Yuan
Entropy
Sparse and Compressive Sensing Techniques
article

TCA-EfficientSCI: A Lightweight Causal Baseline for Cross-Measurement Temporal Continuity in Snapshot Compressive Imaging

Ziheng Cheng, Xing Liu, Mengyuan Liu, Xin Yuan
article en

Abstract

Snapshot compressive imaging (SCI), including coded aperture compressive temporal imaging (CACTI), reconstructs high-speed video frames from compressed low-frame-rate measurements. Most deep SCI reconstruction networks are designed around a measurement-wise formulation: each compressed exposure is reconstructed independently, and the resulting frame segments are concatenated to form a continuous video. This protocol is effective for within-measurement reconstruction, but it leaves cross-measurement temporal continuity largely unmodeled. Boundary artifacts such as flickering, texture drift, or motion jumps can therefore appear between adjacent reconstructed segments, even when frame-wise reconstruction metrics remain competitive. This work identifies and empirically analyzes the underexplored problem of cross-measurement temporal continuity in continuous SCI, and it provides TCA-EfficientSCI as a lightweight, causal, and reproducible baseline. The Temporal Context Adapter uses the last m reconstructed frames from the previous measurement as causal temporal context and injects this history through a gated residual feature pathway. A boundary consistency loss regularizes the predicted temporal variation across measurement boundaries without forcing adjacent frames to be identical. In a controlled three-seed comparison, Full TCA with boundary loss reduces mean Boundary Difference Error (BDE) by 2.23% relative to the matched-epoch EfficientSCI control while maintaining similar PSNR and SSIM. Correct-history inference gives BDE 0.01615, while zero and shuffled history give 0.01725 and 0.01810, respectively. The adapter adds 1,019,905 parameters, or 11.56% relative to the EfficientSCI baseline parameters, and it changes 256×256 mean latency from 54.35 ms to 68.58 ms per measurement in the profiling protocol. Rather than claiming broad reconstruction-quality improvement, this study highlights cross-measurement continuity as an important evaluation and design dimension for continuous SCI deployment.

EntropyVol. 28(7)
Westlake University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 8%
Sparse and Compressive Sensing Techniques
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