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Fully Learnable Model for Task-Driven Image Compressed Sensing.

Bowen Zheng1, Jianping Zhang2, Guiling Sun1

  • 1College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.

Sensors (Basel, Switzerland)
|July 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a fully learnable model for task-driven image-compressed sensing (FLCS) using convolutional neural networks (CNNs). FLCS enhances image acquisition efficiency at low sampling rates, significantly improving reconstructed image quality and reducing processing time.

Keywords:
compressed sensingconvolutional neural networksdeep learningimage reconstruction

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Signal Processing

Background:

  • Efficient image acquisition is crucial for energy-limited systems.
  • Traditional compressed sensing methods often require manual tuning and may not be optimal for specific tasks.
  • Convolutional Neural Networks (CNNs) have shown promise in image reconstruction.

Purpose of the Study:

  • To propose a fully learnable model for task-driven image-compressed sensing (FLCS) at low sampling rates.
  • To improve image acquisition efficiency and reconstructed image quality.
  • To develop a model adaptable to various application scenarios.

Main Methods:

  • A novel FLCS model inspired by compressed sensing was developed.
  • The model integrates Deep Convolution Generative Adversarial Networks (DCGAN) and Variational Auto-encoder (VAE).
  • It comprises three learnable components: Sampler, Solver, and Rebuilder, trained jointly or individually.

Main Results:

  • The proposed FLCS model significantly enhances reconstructed image quality compared to existing methods.
  • It achieves this improvement while reducing the overall running time.
  • The Sampler learns task-specific measurement matrices, and the Rebuilder maps latent representations back to image space.

Conclusions:

  • The FLCS model offers a significant advancement for image-compressed sensing at low sampling rates.
  • It provides a flexible and efficient approach for various applications, particularly in energy-constrained environments.
  • This work holds great significance for the practical application of efficient image sensing technologies.