Adaptive Sampling for Image Compressed Sensing Based on Deep Learning

The compressed sensing (CS) theory has been applied to image compression successfully as most image signals are sparse in a certain domain. In this paper, we focus on how to improve the sampling efficiency for network-based image compressed sensing by using our proposed adaptive sampling algorithm. We conduct content adaptive sampling to achieve a significant improvement. Experiments results indicate that our proposed framework outperforms the state-of-the-arts both in subjective and objective quality. An average of 1-6 dB improvement in peak signal to noise ratio (PSNR) is observed. Moreover, the proposed work reconstructs images with more details and less image blocking effects, leading to apparent visual improvement.

• Publication year

  2019

• Venue

  Journal of Physics: Conference Series

• Publication date

  2019-05-01

• Fields of study

  Physics, Computer Science, Engineering

• Identifiers
  DOI 10.1088/1742-6596/1229/1/012016
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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