Sampling Innovation-Based Adaptive Compressive Sensing
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
Scene-aware Adaptive Compressive Sensing (ACS) has attracted significant
interest due to its promising capability for efficient and high-fidelity
acquisition of scene images. ACS typically prescribes adaptive sampling
allocation (ASA) based on previous samples in the absence of ground truth.
However, when confronting unknown scenes, existing ACS methods often lack
accurate judgment and robust feedback mechanisms for ASA, thus limiting the
high-fidelity sensing of the scene. In this paper, we introduce a Sampling
Innovation-Based ACS (SIB-ACS) method that can effectively identify and
allocate sampling to challenging image reconstruction areas, culminating in
high-fidelity image reconstruction. An innovation criterion is proposed to
judge ASA by predicting the decrease in image reconstruction error attributable
to sampling increments, thereby directing more samples towards regions where
the reconstruction error diminishes significantly. A sampling innovation-guided
multi-stage adaptive sampling (AS) framework is proposed, which iteratively
refines the ASA through a multi-stage feedback process. For image
reconstruction, we propose a Principal Component Compressed Domain Network
(PCCD-Net), which efficiently and faithfully reconstructs images under AS
scenarios. Extensive experiments demonstrate that the proposed SIB-ACS method
significantly outperforms the state-of-the-art methods in terms of image
reconstruction fidelity and visual effects. Codes are available at
https://github.com/giant-pandada/SIB-ACS_CVPR2025.
