|Title:||Deep color mismatch correction in stereoscopic 3D images||Authors:||Croci, Simone
|UNESCO Clasification:||220990 Tratamiento digital. Imágenes||Keywords:||Color Correction
Convolutional Neural Network
|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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