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Blind Correction of Lateral Chromatic Aberration in Raw Bayer Data
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-9947-1088
Sony Mobile Communications AB, SWE.
2021 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 9Article in journal (Refereed) Published
Abstract [en]

Chromatic aberration is an error that occurs in color images due to the fact that camera lenses refract the light of different wavelengths in different angles. The common approach today to correct the error is to use a lookup table for each camera-lens combination, e.g., as in Adobe PhotoShop Lightroom or DxO Optics Pro. In this paper, we propose a method that corrects the chromatic aberration error without any priot knowledge of the camera-lens combination, and does the correction already on the bayer data, i.e., before the Raw image data is interpolated to an RGB image. We evaluate our method in comparison to DxO Optics Pro, a state-of-the-art tool based on lookup tables, using 25 test images and the variance of the color differences (VCD) metric. The results show that our blind method has a similar error correction performance as DxO Optics Pro, but without prior knowledge of the camera-lens setup. CCBYNCND

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021. Vol. 9
Keywords [en]
Blind correction, Cameras, Chromatic aberration, Color, GPGPU, Image color analysis, Image edge detection, Image enhancement, Interpolation, Lenses, Optics, Structural instability, Camera lenses, Error correction, Table lookup, Adobe Photoshop, Color difference, Correction performance, Lens combination, Prior knowledge, Raw image data, State of the art, Aberrations
National Category
Media Engineering
Identifiers
URN: urn:nbn:se:bth-22007DOI: 10.1109/ACCESS.2021.3096201ISI: 000675195700001Scopus ID: 2-s2.0-85110877841OAI: oai:DiVA.org:bth-22007DiVA, id: diva2:1584480
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge Foundation
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2021-08-12 Created: 2021-08-12 Last updated: 2021-09-02Bibliographically approved

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Petersson, StefanGrahn, Håkan

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