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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.

IEEE Xplore, 3876-3885 (2023)
Verlagsversion 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: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Deconvolution ; Microscopy ; Self Supervised ; Zero Shot; Algorithm; Removal
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2375-9232
e-ISSN 2375-9259
Konferenztitel Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
Zeitschrift IEEE Xplore
Quellenangaben Band: , Heft: , Seiten: 3876-3885 Artikelnummer: , Supplement: ,
Verlag IEEE
Verlagsort 10662 Los Vaqueros Circle, Po Box 3014, Los Alamitos, Ca 90720-1264 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530006-001
Förderungen Helmholtz Association under the joint research school "Munich School for Data Science -MUDS"
DOI 10.1109/ICCVW60793.2023.00419
WOS ID 001156680303106
Scopus ID 85182927046
Erfassungsdatum 2024-01-29
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