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Image Compressed Sensing Using Convolutional Neural Network | IEEE Journals & Magazine | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. The article proposes an image compressed sensing framework using convolutional neural networks (CSNet) that includes a sampling network and a reconstruction network.
2. CSNet adaptively learns the sampling matrix from training images, which makes the CS measurements retain more image structural information for better reconstruction.
3. Experimental results demonstrate that CSNet offers state-of-the-art reconstruction quality, while achieving fast running speed.
Article analysis:
The article is generally reliable and trustworthy as it provides detailed information about the proposed image compressed sensing framework using convolutional neural networks (CSNet). The article also provides evidence to support its claims, such as experimental results demonstrating that CSNet offers state-of-the-art reconstruction quality, while achieving fast running speed. However, there are some potential biases in the article that should be noted. For example, the article does not explore any counterarguments or alternative approaches to image compression and reconstruction. Additionally, the article does not discuss any possible risks associated with using this approach or provide any insight into how it could be improved upon in future research. Furthermore, the article does not present both sides of the argument equally; instead it focuses solely on promoting its own approach without considering other perspectives or solutions.