… Compressed sensing (CS) emerged, which is able to satisfy the needs of transmission
efficiency and security at the same time. Compressed sensing is an advanced method of …

… In short, we define two projection operators toward image prior and data consistency, … and
its medical applications is how to depict the image prior. Based on the framework, we analyze …

… Compressed sensing recovery is a linear optimization problem. The most common CS retrieval
algorithms explore the prior knowledge that a natural image … understand and implement; …

… the image prior and thus … compressed measurement to the reconstruction and the adversarial
learning manner. In order to evaluate the benefits of these three strengths, we implement …

… As of ReconNet and I-ReconNet, we redo the programming implementation according to the
… He, and RC de Lamare, “Design of compressed sensing system with probability-based prior …

… sensing matrices in our application of image compression. For example, (1) can be implemented
by performing a 2D-DCT on the image … earlier, the performance of a compression sys…

… in a block-by-block manner that leads to serious block artifacts or train the deep network as
a black box that brings about limited insights of image prior knowledge. In this paper, a novel …

… prior knowledge that an HSI can be sparsely represented under an appropriate redundant
dictionary, Duarte and Baraniuk [34] proposed a compressive sensing … methods implemented …

… Our prior work shows that posterior sampling is instance-optimal for compressed sensing
[45… We use the publicly available implementation from the BART toolbox [88, 86] and optimize …

… model by basic prior knowledge. Random sampling is implemented in each subimage. …
paper is much better than the former two algorithms in reconstructing nonreference images. …