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Training-free Acceleration for Blind Compressed Image Restoration via Activation-Calibrated Singular Value Decomposition

姚苏杭, Xin Li, Xijun Wang, Yufei Wang, Yeying Jin, Zhibo Chen · 2026 · IEEE Journal on Emerging and Selected Topics in Circuits and Systems

DOI: 10.1109/jetcas.2026.3745896 · cited by 0 works (OpenAlex) · open-access PDF

Korean

트랜스포머 기반 21개 유형의 트랜스포머 기반 활동 모델은 알려지지 않은 압축 설정을 통해 눈에 띄는 압축 이미지 복구(CIR)에서 강력한 성능을 기록하지만, 그들의 밀집된 프로젝션 운영자는 비용이 비싸게 배포를 수행합니다. 우리는 경험적이고 운영자 수준의 분석을 통해 그러한 모델에 대한 훈련없는 가속화를 연구합니다. 구체적으로, 우리는 컴퓨팅이 집중되어 있는 곳에서 채널 방향 활성화 배포를 압축된 입력하에 특징화하고 대표적인 프로젝션 무게의 단일 값 분해 (TSVD)를 검사합니다. 이러한 측정은 관심, 피드-프로워드, 콘플로우션 및 프롬프트 조정 경로에 반복되는 프로젝션을 식별

Machine translation (Meta M2M100); the English original follows.

Abstract

Transformer-based models achieve strong performance on blind compressed image restoration (CIR) under unknown compression settings, but their densely stacked projection operators make deployment costly. We study training-free acceleration for such models through an empirical, operator-level analysis. Specifically, we profile where computation is concentrated, characterize channel-wise activation distributions under compressed inputs, and examine the singular-value decay of representative projection weights. These measurements identify the recurring projections in the attention, feed-forward, convolutional, and prompt-conditioning pathways as effective low-rank replacement targets that preserve the original inference graph. They further establish truncated SVD (TSVD) as a principled training-free baseline for blind CIR and reveal why a uniform low-rank objective is mismatched to the channel-imbalanced feature distributions of compressed images. Accordingly, we formulate Activation-Calibrated Singular Value Decomposition (ASVD) for this restoration setting, deriving low-rank factors entirely offline from pretrained weights and lightweight activation statistics gathered from only a few compressed calibration images. By weighting the decomposition with channel-wise activation magnitudes, our ASVD assigns larger penalties to errors on more active channels, requiring neither retraining nor online calibration and leaving the restoration pipeline intact. When instantiated on PromptCIR at rank 64, ASVD consistently improves PSNR-B over TSVD across four standard benchmarks under identical parameter and MAC budgets. At this operating point, the factorized model cuts parameters by 59% (34.75M to 14.34M) and computation by 16.1% (258.79 to 217.16 GMACs for a 256 x 256 input), enabling acceleration without retraining.

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Topics: Singular value decomposition, Projection (relational algebra), Computation, Acceleration, Pipeline (software), Compressed sensing

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