Showing papers by "Jianchao Yang published in 2008"

Image super-resolution as sparse representation of raw image patches

Abstract: This paper addresses the problem of generating a super-resolution (SR) image from a single low-resolution input image. We approach this problem from the perspective of compressed sensing. The low-resolution image is viewed as downsampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signal-atoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the downsampled signal. We will demonstrate the effectiveness of sparsity as a prior for regularizing the otherwise ill-posed super-resolution problem. We further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high-resolution image is competitive or even superior in quality to images produced by other SR methods.

Face hallucination VIA sparse coding

Abstract: In this paper, we address the problem of hallucinating a high resolution face given a low resolution input face. The problem is approached through sparse coding. To exploit the facial structure, non-negative matrix factorization (NMF) is first employed to learn a localized part-based subspace. This subspace is effective for super-resolving the incoming low resolution face under reconstruction constraints. To further enhance the detailed facial information, we propose a local patch method based on sparse representation with respect to coupled overcomplete patch dictionaries, which can be fast solved through linear programming. Experiments demonstrate that our approach can hallucinate high quality super-resolution faces.

Non-negative graph embedding

Abstract: We introduce a general formulation, called non-negative graph embedding, for non-negative data decomposition by integrating the characteristics of both intrinsic and penalty graphs [17]. In the past, such a decomposition was obtained mostly in an unsupervised manner, such as Non-negative Matrix Factorization (NMF) and its variants, and hence unnecessary to be powerful at classification. In this work, the non-negative data decomposition is studied in a unified way applicable for both unsupervised and supervised/semi-supervised configurations. The ultimate data decomposition is separated into two parts, which separatively preserve the similarities measured by the intrinsic and penalty graphs, and together minimize the data reconstruction error. An iterative procedure is derived for such a purpose, and the algorithmic non-negativity is guaranteed by the non-negative property of the inverse of any M-matrix. Extensive experiments compared with NMF and conventional solutions for graph embedding demonstrate the algorithmic properties in sparsity, classification power, and robustness to image occlusions.

Surveillance Event Detection.

Abstract: We have developed and evaluated three generalized systems for event detection. The first system is a simple brute force search method, where each space-time location in the video is evaluated by a binary decision rule on whether it contains the event or not. The second system is build on top of a head tracker to avoid costly brute force searching. The decision stage is a combination of state of the art feature extractors and classifiers. Our third system has a probabilistic framework. From the observations, the pose of the people are estimated and used to determine the presence of event. Finally we introduce two ad-hoc methods that were designed to specifically detect OpposingFlow and TakePicture events. The results are promising as we are able to get good results on several event categories, while for all events we have gained valuable insights and experience.