Markov random field modeling in computer vision
Markov random field modeling in computer vision
Video matting of complex scenes
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Optical flow based super-resolution: A probabilistic approach
Computer Vision and Image Understanding
Exploring Defocus Matting: Nonparametric Acceleration, Super-Resolution, and Off-Center Matting
IEEE Computer Graphics and Applications
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Resolution Enhancement of PMD Range Maps
Proceedings of the 30th DAGM symposium on Pattern Recognition
SoftCuts: a soft edge smoothness prior for color image super-resolution
IEEE Transactions on Image Processing
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution
IEEE Transactions on Image Processing
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Super-resolution of the alpha matte and the foreground object from a video are jointly attempted in this paper. Instead of super-resolving them independently, we treat super-resolution of the matte and foreground in a combined framework, incorporating the matting equation in the image degradation model. We take multiple adjacent frames from a low-resolution video with non-global motion for increasing the spatial resolution. This ill-posed problem is regularized by employing a Bayesian restoration approach, wherein the high-resolution image is modeled as a Markov Random Field. In matte super-resolution, it is particularly important to preserve fine details at the boundary pixels between the foreground and background. For this purpose, we use a discontinuityadaptive smoothness prior to include observed data in the solution. This framework is useful in video editing applications for compositing low-resolution objects into high-resolution videos.