Paper ID | TEC-3.4 | ||
Paper Title | DEEP COLOR MISMATCH CORRECTION IN STEREOSCOPIC 3D IMAGES | ||
Authors | Simone Croci, Trinity College Dublin, Ireland; Cagri Ozcinar, Samsung Research Institute UK, United Kingdom; Emin Zerman, Trinity College Dublin, Ireland; Roman Dudek, Universidad de Las Palmas de Gran Canaria, Spain; Sebastian Knorr, Ernst Abbe University of Applied Sciences Jena, Germany; Aljosa Smolic, Trinity College Dublin, Ireland | ||
Session | TEC-3: Restoration and Enhancement 3 | ||
Location | Area G | ||
Session Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
Presentation Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
Presentation | Poster | ||
Topic | Image and Video Processing: Restoration and enhancement | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
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. |