scispace - formally typeset
Search or ask a question

Showing papers by "Jian Sun published in 2003"


Journal ArticleDOI
TL;DR: This paper formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation to obtain the maximum a posteriori (MAP) estimation in the Markovnetwork.
Abstract: In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, and a binary process for occlusion. After eliminating the line process and the binary process by introducing two robust functions, we apply the belief propagation algorithm to obtain the maximum a posteriori (MAP) estimation in the Markov network. Other low-level visual cues (e.g., image segmentation) can also be easily incorporated in our stereo model to obtain better stereo results. Experiments demonstrate that our methods are comparable to the state-of-the-art stereo algorithms for many test cases.

1,272 citations


Proceedings ArticleDOI
18 Jun 2003
TL;DR: This work is inspired by recent progress on natural image statistics that the priors of image primitives can be well represented by examples and proposes a Bayesian approach to image hallucination, where primal sketch priors are constructed and used to enhance the quality of the hallucinated high resolution image.
Abstract: We propose a Bayesian approach to image hallucination. Given a generic low resolution image, we hallucinate a high resolution image using a set of training images. Our work is inspired by recent progress on natural image statistics that the priors of image primitives can be well represented by examples. Specifically, primal sketch priors (e.g., edges, ridges and corners) are constructed and used to enhance the quality of the hallucinated high resolution image. Moreover, a contour smoothness constraint enforces consistency of primitives in the hallucinated image by a Markov-chain based inference algorithm. A reconstruction constraint is also applied to further improve the quality of the hallucinated image. Experiments demonstrate that our approach can hallucinate high quality super-resolution images.

444 citations


Journal ArticleDOI
TL;DR: A new modified EM algorithm is constructed that is efficient for unsupervised color image segmentation and developed through the singular value decomposition and the Cholesky decomposition.

154 citations


01 Jun 2003
TL;DR: Zhang et al. as mentioned in this paper proposed a Bayesian approach to image hallucination by constructing primal sketch priors (e.g., edges, ridges and corners) to enhance the quality of the hallucinated high resolution image.
Abstract: In this paper, we propose a Bayesian approach to image hallucination. Given a generic low resolution image, we hallucinate a high resolution image using a set of training images. Our work is inspired by recent progress on natural image statistics that the priors of image primitives can be well represented by examples. Specifically, primal sketch priors (e.g., edges, ridges and corners) are constructed and used to enhance the quality of the hallucinated high resolution image. Moreover, a contour smoothness constraint enforces consistency of primitives in the hallucinated image by a Markov-chain based inference algorithm. A reconstruction constraint is also applied to further improve the quality of the hallucinated image. Experiments demonstrate that our approach can hallucinate high quality super- resolution images.

17 citations