Skip to main content

Deep Color Mismatch Correction In Stereoscopic 3D Images

Simone Croci, Cagri Ozcinar, Emin Zerman, Roman Dudek, Sebastian Knorr, Aljosa Smolic

  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
    Length: 00:09:31
22 Sep 2021

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.

Value-Added Bundle(s) Including this Product

More Like This

  • SPS
    Members: Free
    IEEE Members: $25.00
    Non-members: $40.00
  • SPS
    Members: Free
    IEEE Members: $25.00
    Non-members: $40.00