Semantic-Aware Image Compressed Sensing
Bowen Zhang, Zhijin Qin, Geoffrey Ye Li

TL;DR
This paper introduces a semantic-aware image compressed sensing system that adaptively determines measurement matrices based on image content, improving sensing efficiency and reconstruction quality over traditional fixed-matrix methods.
Contribution
The paper presents a novel semantic-aware CS system with a policy network for adaptive measurement matrix selection and a specialized reconstruction network, trained end-to-end with a rate-distortion loss.
Findings
Outperforms traditional fixed-matrix CS systems in numerical tests.
Adaptive measurement matrix selection improves sensing efficiency.
Joint training with rate-distortion loss enhances reconstruction quality.
Abstract
Deep learning based image compressed sensing (CS) has achieved great success. However, existing CS systems mainly adopt a fixed measurement matrix to images, ignoring the fact the optimal measurement numbers and bases are different for different images. To further improve the sensing efficiency, we propose a novel semantic-aware image CS system. In our system, the encoder first uses a fixed number of base CS measurements to sense different images. According to the base CS results, the encoder then employs a policy network to analyze the semantic information in images and determines the measurement matrix for different image areas. At the decoder side, a semantic-aware initial reconstruction network is developed to deal with the changes of measurement matrices used at the encoder. A rate-distortion training loss is further introduced to dynamically adjust the average compression ratio…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Image Processing Techniques and Applications · Photoacoustic and Ultrasonic Imaging
MethodsBalanced Selection
