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.

翻译：场景感知自适应压缩感知（ACS）因其在高效、高保真获取场景图像方面的潜力而受到广泛关注。ACS通常在缺乏真实值的情况下，基于先前的采样结果来制定自适应采样分配（ASA）策略。然而，面对未知场景时，现有的ACS方法往往缺乏对ASA的准确判断和鲁棒的反馈机制，从而限制了场景的高保真感知。本文提出了一种基于采样创新的自适应压缩感知（SIB-ACS）方法，该方法能够有效识别图像重建中的困难区域并分配采样资源，最终实现高保真的图像重建。我们提出了一种创新准则，通过预测因采样增量带来的图像重建误差下降来判断ASA，从而将更多采样资源导向重建误差显著减小的区域。同时，我们提出了一种采样创新引导的多阶段自适应采样（AS）框架，该框架通过多阶段反馈过程迭代优化ASA策略。针对图像重建，我们提出了一种主成分压缩域网络（PCCD-Net），能够在AS场景下高效且忠实地重建图像。大量实验表明，所提出的SIB-ACS方法在图像重建保真度和视觉效果方面均显著优于现有最先进方法。代码公开于 https://github.com/giant-pandada/SIB-ACS_CVPR2025。