Dual-Path Image Reconstruction: Bridging Vision Transformer and Perceptual Compressive Sensing Networks

• Autorzy: Bairi Zakaria , Bey Kadda Beghdad , Ben-Ahmed Olfa , Amamra Abdenour , Bradai Abbas

Identyfikatory

DOI

10.15439/2023F978

• Języki publikacji: EN

Abstrakty

EN

Over the past few years, notable advancements have been made through the adoption of self-attention mechanisms and perceptual optimization, which have proven to be successful techniques in enhancing the overall quality of image reconstruction. Self-attention mechanisms in Vision Transformers have been widely used in neural networks to capture long-range dependencies in image data, while perceptual optimization has been shown to enhance the perceptual quality of reconstructed images. In this paper, we present a novel approach to image reconstruction by bridging the capabilities of Vision Transformer and Perceptual Compressive Sensing Networks. Specifically, we use a self-attention mechanism to capture the global context of the image and guide the sampling process, while optimizing the perceptual quality of the sampled image using a pre-trained perceptual loss function. Our experiments demonstrate that our proposed approach outperforms existing state-of-the-art methods in terms of reconstruction quality and achieves visually pleasing results. Overall, our work contributes to the development of efficient and effective techniques for image sampling and reconstruction, which have potential applications in a wide range of domains, including medical imaging and video processing.

Słowa kluczowe

EN

visualization; technological innovation; neural networks; image sampling; streaming media; transformer; image reconstruction

PL

wizualizacja; innowacje technologiczne; sieci neuronowe; próbkowanie obrazu; media strumieniowe; transformator; rekonstrukcja obrazu

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 347--354
• Opis fizyczny: Bibliogr. 31 poz., wz., il., tab., wykr.

Twórcy

autor

Bairi Zakaria

• Ecole Militaire Polytechnique, Bordj El Behri, Algiers, Algeria

autor

Bey Kadda Beghdad

• Ecole Militaire Polytechnique, Bordj El Behri, Algiers, Algeria

autor

Ben-Ahmed Olfa

• XLIM Research Institute, UMR CNRS 7252 University of Poitiers, France

autor

Amamra Abdenour

• Ecole Militaire Polytechnique, Bordj El Behri, Algiers, Algeria

autor

Bradai Abbas

• XLIM Research Institute, UMR CNRS 7252 University of Poitiers, France

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Uwagi

1. Thematic Tracks Regular Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).