TL;DR
COAST is a deep learning model for compressive sensing that can handle arbitrary sampling matrices with a single trained model, offering interpretability, efficiency, and state-of-the-art performance across diverse sampling scenarios.
Contribution
The paper introduces COAST, a novel deep unfolding network that enables arbitrary sampling in compressive sensing with one model, incorporating RPA, CPMM, and PnP-D modules.
Findings
Handles arbitrary sampling matrices with one model
Achieves state-of-the-art performance on benchmarks
Operates with fast speed
Abstract
Recent deep network-based compressive sensing (CS) methods have achieved great success. However, most of them regard different sampling matrices as different independent tasks and need to train a specific model for each target sampling matrix. Such practices give rise to inefficiency in computing and suffer from poor generalization ability. In this paper, we propose a novel COntrollable Arbitrary-Sampling neTwork, dubbed COAST, to solve CS problems of arbitrary-sampling matrices (including unseen sampling matrices) with one single model. Under the optimization-inspired deep unfolding framework, our COAST exhibits good interpretability. In COAST, a random projection augmentation (RPA) strategy is proposed to promote the training diversity in the sampling space to enable arbitrary sampling, and a controllable proximal mapping module (CPMM) and a plug-and-play deblocking (PnP-D) strategy…
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