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Chobola, T. ; Müller, G.* ; Dausmann, V.* ; Theileis, A.* ; Taucher, J.* ; Huisken, J.* ; Peng, T.

Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration.

In: (Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023). 10662 Los Vaqueros Circle, Po Box 3014, Los Alamitos, Ca 90720-1264 Usa: Ieee Computer Soc, 2023. 3876-3885 (Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023)
DOI
Non-blind deconvolution aims to restore a sharp image from its blurred counterpart given an obtained kernel. Existing deep neural architectures are often built based on large datasets of sharp ground truth images and trained with supervision. Sharp, high quality ground truth images, however, are not always available, especially for biomedical applications. This severely hampers the applicability of current approaches in practice. In this paper, we propose a novel non-blind deconvolution method that leverages the power of deep learning and classic iterative deconvolution algorithms. Our approach combines a pre-trained network to extract deep features from the input image with iterative Richardson-Lucy deconvolution steps. Subsequently, a zero-shot optimisation process is employed to integrate the deconvolved features, resulting in a high-quality reconstructed image. By performing the preliminary reconstruction with the classic iterative deconvolution method, we can effectively utilise a smaller network to produce the final image, thus accelerating the reconstruction whilst reducing the demand for valuable computational resources. Our method demonstrates significant improvements in various real-world applications non-blind deconvolution tasks.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Deconvolution ; Microscopy ; Self Supervised ; Zero Shot; Algorithm; Removal
ISSN (print) / ISBN 9798350307443
Konferenztitel Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
Quellenangaben Band: , Heft: , Seiten: 3876-3885 Artikelnummer: , Supplement: ,
Verlag Ieee Computer Soc
Verlagsort 10662 Los Vaqueros Circle, Po Box 3014, Los Alamitos, Ca 90720-1264 Usa
Nichtpatentliteratur Publikationen
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Förderungen Helmholtz Association under the joint research school "Munich School for Data Science -MUDS"