Adaptive and Cascaded Compressive Sensing
Scene-dependent adaptive compressive sensing (CS) has been a long pursuing goal which has huge potential in significantly improving the performance of CS. However, without accessing to the ground truth image, how to design the scene-dependent adaptive strategy is still an open-problem and the improvement in sampling efficiency is still quite limited. In this paper, a restricted isometry property (RIP) condition based error clamping is proposed, which could directly predict the reconstruction error, i.e. the difference between the currently-stage reconstructed image and the ground truth image, and adaptively allocate samples to different regions at the successive sampling stage. Furthermore, we propose a cascaded feature fusion reconstruction network that could efficiently utilize the information derived from different adaptive sampling stages. The effectiveness of the proposed adaptive and cascaded CS method is demonstrated with extensive quantitative and qualitative results, compared with the state-of-the-art CS algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Compressive SensingSimilar Papers 제목 키워드 기반
Reconstruction-free Cascaded Adaptive Compressive Sensing
Scene-aware Adaptive Compressive Sensing (ACS) has constituted a persistent pursuit holding substantial promise for the enhancement of Compressive Sensing (CS) performance. Cascaded ACS furnishes a proficient multi-s…
Compressive SensingImage ReconstructionImage Classification with A Deep Network Model based on Compressive Sensing
To simplify the parameter of the deep learning network, a cascaded compressive sensing model "CSNet" is implemented for image classification. Firstly, we use cascaded compressive sensing network to learn feature from the…
ClassificationCompressive SensingGeneral Classificationimage-classification+1Robust Deep Compressive Sensing with Recurrent-Residual Structural Constraints
Existing deep compressive sensing (CS) methods either ignore adaptive online optimization or depend on costly iterative optimizer during reconstruction. This work explores a novel image CS framework with recurrent-residu…
Compressive SensingImage Restoration from Patch-based Compressed Sensing Measurement
A series of methods have been proposed to reconstruct an image from compressively sensed random measurement, but most of them have high time complexity and are inappropriate for patch-based compressed sensing capture, be…
compressed sensingCompressive SensingImage ReconstructionImage RestorationReinforcement Learning for Adaptive Video Compressive Sensing
We apply reinforcement learning to video compressive sensing to adapt the compression ratio. Specifically, video snapshot compressive imaging (SCI), which captures high-speed video using a low-speed camera is considered …
Compressive Sensingobject-detectionObject Detectionreinforcement-learning+3