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Adaptive Compressive Sensing of Images Using Spatial Entropy.

Ran Li1, Xiaomeng Duan1, Xiaoli Guo1

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This study introduces Adaptive Block Compressive Sensing (ABCS) to improve image reconstruction quality in wireless sensor networks. ABCS reduces artifacts by adaptively allocating resources based on spatial entropy, enhancing image details.

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Area of Science:

  • Image processing
  • Signal processing
  • Computer vision

Background:

  • Compressive Sensing (CS) offers efficient image encoding for resource-limited wireless sensor networks.
  • Non-stationary image statistics lead to blocking artifacts and blur in CS-based reconstructions.
  • Existing CS methods struggle with maintaining image fidelity due to inherent statistical variations.

Purpose of the Study:

  • To develop an improved image reconstruction technique for Compressive Sensing (CS) in wireless sensor networks.
  • To address blocking artifacts and blurriness in CS-reconstructed images.
  • To enhance the quality of reconstructed images by adaptively allocating measurement resources.

Main Methods:

  • Proposes an Adaptive Block Compressive Sensing (ABCS) system utilizing spatial entropy.
  • Spatial entropy is employed to measure information content and guide resource allocation across image regions.
  • A linear reconstruction mode, using matrix-vector products, is adopted to minimize decoding complexity.

Main Results:

  • The ABCS system demonstrates superior image reconstruction quality compared to standard CS methods.
  • Both subjective and objective evaluations confirm the enhanced fidelity of reconstructed images.
  • The proposed method achieves low decoding complexity, making it suitable for practical applications.

Conclusions:

  • Adaptive Block Compressive Sensing (ABCS) effectively mitigates artifacts in CS image reconstruction.
  • Spatial entropy serves as a robust metric for adaptive resource allocation, preserving image details.
  • The ABCS system offers a promising solution for high-quality, low-complexity image compression in wireless sensor networks.