Parallel image foreground-background reconstruction network for compressed sensing

Yanhe Chen

doi:10.1117/12.2681649

1 June 2023 Parallel image foreground-background reconstruction network for compressed sensing

Yanhe Chen

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127181W (2023) https://doi.org/10.1117/12.2681649
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

Compressed sensing (CS) algorithms for images based on deep learning offer huge improvements over conventional algorithms to reduce time complexity and improve reconstruction quality. but existing deep compressed sensing (DCS) algorithms still have problems, among which our concern is the loss of detail features in image reconstruction. Based on this, We propose a parallel image foreground- background reconstruction network (PFBNet), and PFBNet combines the image segmentation (IS) domain. The initial reconstruction uses parallel networks to learn the features of the segmented foreground and background images, so each network focuses on learning the main features of the image to recover pixel details. Then, the deep network superimposes the foreground and background images into a complete image while performing error elimination to produce the final image. Experimentally, the PFBNet algorithm demonstrates better recovery performance and preserves the pixel and texture information of the image.

Citation Download Citation

Yanhe Chen "Parallel image foreground-background reconstruction network for compressed sensing", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127181W (1 June 2023); https://doi.org/10.1117/12.2681649

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KEYWORDS

Image restoration

Reconstruction algorithms

Image processing

Image segmentation

Education and training

Image quality

Compressed sensing

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