Signals 4 · free daily AI digest

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

Paper recorded by Signals 4 on 2026-10-21 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.CV · 计算机视觉 · first seen 2026-10-22

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 d

Read on arXiv →

#200 most recent of 756 cs.CV papers we have recorded · ↑ newer: Looking Ahead: Modeling Future Evolution in Autoregressive Video Gener · ↓ older: Existence of Minimizers for the Gaussian Mumford-Shah Model via Covari
Cite this page: Training-free Acceleration for Blind Compressed Image Restoration via Activation-Calibrated Singular Value Decomposition: the #200 most recent of 756 cs.CV papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/training-free-acceleration-for-blind-compressed-image-restoration-via-activation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions