Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/114052
Title: Deep color mismatch correction in stereoscopic 3D images
Authors: Croci, Simone
Ozcinar, Cagri
Zerman, Emin
Dudek, Roman 
Knorr, Sebastian
Smolic, Aljosa
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
Keywords: Color Correction
Color Mismatch
Convolutional Neural Network
Stereoscopic 3D
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Journal: Proceedings - International Conference on Image Processing 
Conference: IEEE International Conference on Image Processing (ICIP 2021) 
Abstract: Color mismatch in stereoscopic 3D (S3D) images can create visual discomfort and affect the performance of S3D image processing algorithms, e.g., for depth estimation. In this paper, we propose a new deep learning-based solution for the problem of color mismatch correction. The proposed solution consists of a multi-task convolutional neural network, where color correction is the primary task and correspondence estimation is the secondary task. For the training and evaluation of the proposed network, a new S3D image dataset with color mismatch was created. Based on this dataset, experiments were conducted showing the effectiveness of our solution.
URI: http://hdl.handle.net/10553/114052
ISBN: 978-1-6654-4115-5
ISSN: 1522-4880
DOI: 10.1109/ICIP42928.2021.9506036
Source: Proceedings - International Conference on Image Processing, ICIP [ISSN 1522-4880], v. 2021-September, p. 1749-1753, (Enero 2021)
Appears in Collections:Actas de congresos
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