AutoBCS: Block-based Image Compressive Sensing with Data-driven Acquisition and Non-iterative Reconstruction
Yang Gao, Hongping Gan, Haiwei CHen, Chunyi Liu, Feng Liu

TL;DR
AutoBCS introduces a deep learning-based block compressive sensing framework that uses data-driven sensing matrices and a non-iterative reconstruction network, achieving faster and higher-quality image reconstruction.
Contribution
It proposes a novel learning-based sensing matrix satisfying CS theory and a non-iterative reconstruction model, enhancing efficiency and image quality in block compressive sensing.
Findings
Outperforms traditional BCS methods in SSIM and PSNR.
Provides faster reconstruction with lower computational complexity.
Achieves superior visual quality and artifact reduction.
Abstract
Block compressive sensing is a well-known signal acquisition and reconstruction paradigm with widespread application prospects in science, engineering and cybernetic systems. However, state-of-the-art block-based image compressive sensing (BCS) methods generally suffer from two issues. The sparsifying domain and the sensing matrices widely used for image acquisition are not data-driven, and thus both the features of the image and the relationships among subblock images are ignored. Moreover, doing so requires addressing high-dimensional optimization problems with extensive computational complexity for image reconstruction. In this paper, we provide a deep learning strategy for BCS, called AutoBCS, which takes the prior knowledge of images into account in the acquisition step and establishes a subsequent reconstruction model for performing fast image reconstruction with a low…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Image and Signal Denoising Methods · Blind Source Separation Techniques
