Shengyang Dai

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Effective image prior is necessary for image super resolution, due to its severely under-determined nature. Although the edge smoothness prior can be effective, it is generally difficult to have analytical forms to evaluate the edge smoothness, especially for soft edges that exhibit gradual intensity transitions. This paper finds the connection between the(More)
Motion blur retains some information about motion, based on which motion may be recovered from blurred images. This is a difficult problem, as the situations of motion blur can be quite complicated, such as they may be space-variant, nonlinear, and local. This paper addresses a very challenging problem: can we recover motion blindly from a single(More)
Designing effective image priors is of great interest to image super-resolution (SR), which is a severely under-determined problem. An edge smoothness prior is favored since it is able to suppress the jagged edge artifact effectively. However, for soft image edges with gradual intensity transitions, it is generally difficult to obtain analytical forms for(More)
In this paper, a novel algorithm for single image super resolution is proposed. Back-projection [1] can minimize the reconstruction error with an efficient iterative procedure. Although it can produce visually appealing result, this method suffers from the chessboard effect and ringing effect, especially along strong edges. The underlining reason is that(More)
Removing image partial blur is of great practical importance. However, as existing recovery techniques usually assume a one-layer clear image model, they can not characterize the actual generation process of partial blurs. In this paper, a two-layer image model is investigated. Based on the study of partial blur generation process, a novel recovery(More)
Many emerging applications require tracking targets in video. Most existing visual tracking methods do not work well when the target is motion-blurred (especially due to fast motion), because the imperfectness of the target's appearances invalidates the image matching model (or the measurement model) in tracking. This paper presents a novel method to track(More)
Component-based detection methods have demonstrated their promise by integrating a set of part-detectors to deal with large appearance variations of the target. However, an essential and critical issue, i.e., how to handle the imperfectness of part-detectors in the integration, is not well addressed in the literature. This paper proposes a detector ensemble(More)
In this paper, we propose an MRF-based deinterlacing algorithm that combines the benefits of rule-based algorithms such as motion-adaptation, edge-directed interpolation, and motion compensation, with those of an MRF formulation. MRF-based interpolation and enhancement algorithms are typically formulated as an optimization over pixel intensities or colors,(More)
Identifying space-variant motion blurs is a very challenging task in blind blur identification research. This paper describes a novel method towards blind identification without deblurring. Based on the image gradients in the alpha-channel component of a blurred color image, an elegant alpha-motion blur constraint is proposed, which is a linear constraint(More)