Markov random field modeling in computer vision
Markov random field modeling in computer vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Shape and the Stereo Correspondence Problem
International Journal of Computer Vision
Fuzzy-adapted linear interpolation algorithm for image zooming
Signal Processing
Saliency-directed image interpolation using particle swarm optimization
Signal Processing
Stochastic super-resolution image reconstruction
Journal of Visual Communication and Image Representation
Super resolution of images of 3D scenecs
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Rethinking the prior model for stereo
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Total variation blind deconvolution
IEEE Transactions on Image Processing
A Robust and Computationally Efficient Simultaneous Super-Resolution Scheme for Image Sequences
IEEE Transactions on Circuits and Systems for Video Technology
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Simultaneous interpolation of stereo images aims to use a pair of stereo images to reconstruct another pair of images with higher resolutions. To tackle this problem, a simultaneous approach is proposed in this paper. Since the prior image model is important for solving the ill-posed numerical issues encountered in the image interpolation computation, the proposed approach exploits a prior image model that considers both the spatial local smoothness constraint within each reconstructed high-resolution image and the disparity-compensated local smoothness constraint between the pair of reconstructed high-resolution images. The proposed approach requires the disparity to be known in advance or has been accurately estimated by the existing stereo matching algorithm. Experiments are conducted using both artificially generated images and real-life images to demonstrate the superior performance of the proposed approach.