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Raw or Cooked?: Object Detection on RAW Images
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Zenseact, Gothenburg, Sweden.ORCID iD: 0000-0002-0194-6346
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Zenseact, Gothenburg, Sweden.ORCID iD: 0000-0003-2553-3367
Zenseact, Gothenburg, Sweden.ORCID iD: 0000-0002-9203-558X
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
2023 (English)In: Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I. / [ed] Rikke Gade, Michael Felsberg, Joni-Kristian Kämäräinen, Springer, 2023, Vol. 13885, p. 374-385Conference paper, Published paper (Refereed)
Abstract [en]

Images fed to a deep neural network have in general undergone several handcrafted image signal processing (ISP) operations, all of which have been optimized to produce visually pleasing images. In this work, we investigate the hypothesis that the intermediate representation of visually pleasing images is sub-optimal for downstream computer vision tasks compared to the RAW image representation. We suggest that the operations of the ISP instead should be optimized towards the end task, by learning the parameters of the operations jointly during training. We extend previous works on this topic and propose a new learnable operation that enables an object detector to achieve superior performance when compared to both previous works and traditional RGB images. In experiments on the open PASCALRAW dataset, we empirically confirm our hypothesis.

Place, publisher, year, edition, pages
Springer, 2023. Vol. 13885, p. 374-385
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13885
Keywords [en]
Computer Vision, Object detection, RAW images, Image Signal Processing
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-199000DOI: 10.1007/978-3-031-31435-3_25Scopus ID: 2-s2.0-85161382246ISBN: 9783031314346 (print)ISBN: 9783031314353 (electronic)OAI: oai:DiVA.org:liu-199000DiVA, id: diva2:1809798
Conference
Scandinavian Conference on Image Analysis, Sirkka, Finland, April 18–21, 2023
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2023-11-06 Created: 2023-11-06 Last updated: 2023-11-09Bibliographically approved

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Ljungbergh, WilliamJohnander, JoakimFelsberg, Michael

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