Patch-Based Disparity Remapping for Stereoscopic Images

Post-production and processing for stereoscopic 3D are attracting a lot attention in recent years. In particular, the acquired disparity in most situations requires further manipulation to adjust different view conditions. This paper proposes a novel method to address the issue of disparity remapping of stereoscopic images. We present a nonlinear disparity mapping model to adjust the depth range of the whole image as well as the special visual important regions. To implement this model, our method compute saliency maps for the stereoscopic images. Then we extend the PatchMatch algorithm to search for the proper patches in both the left and the right images by visual combined constraints, and use them to iteratively refine the images to meet the target depth range. Our method is capable of minimizing the distortion of the images and ensuring the correct stereo consistency after disparity remapping. The experimental results demonstrate that the proposed approach can adjust the depth range to improve the stereoscopic effects while preserving the naturalness of the scene.