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A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a(More)
We propose an image interpolation algorithm that is nonparametric and learning-based, primarily using an adaptive k-nearest neighbor algorithm with global considerations through Markov random fields. The empirical nature of the proposed algorithm ensures image results that are data-driven and, hence, reflect "real-world" images well, given enough training(More)
Motivated by the success of support vector regression (SVR) in blind image deconvolution, we apply SVR to single-frame super-resolution. Initial results show that even when trained on as little as a single image, SVR is able to learn a generally applicable model that can super-resolve dissimilar images.
As abstract representations of relational data, graphs and networks find wide use in a variety of fields, particularly when working in non-Euclidean spaces. Yet for graphs to be truly useful in in the context of signal processing, one ultimately must have access to flexible and tractable statistical models. One model currently in use is the Chung-Lu random(More)
This paper proposes the application of learned kernels in support vector regression to superresolution in the discrete cosine transform (DCT) domain. Though previous works involve kernel learning, their problem formulation is examined to reformulate the semi-definite programming problem of finding the optimal kernel matrix. For the particular application to(More)
This work considers a combination classification-regression based framework with the proposal of using learned kernels in modified support vector regression to provide superresolution. The usage of both generative and discriminative learning techniques is examined first by assuming a distribution for image content for classification and then providing(More)
The proposed algorithm in this work provides superresolution for color images by using a learning based technique that utilizes both generative and discriminant approaches. The combination of the two approaches is designed with a stochastic classification-regression framework where a color image patch is first classified by its content, and then, based on(More)
3D point cloud registration is traditionally done by aligning to known information. This information can be extracted from semantically labeled and geo-registered 2D images, e.g. maps, satellite images, and labeled aerial photos. We propose an automated method to geo-register 3D point clouds to 2D maps by defining a normalized Hough similarity function and(More)
An empirical study of the domain of patch-based learning algorithms for image and video processing is conducted. As patch-based algorithms are commonly used, knowledge of the properties of fixed size image patches would prove particularly useful and interesting. We are concerned with investigating the overall distribution of vectorized patches of general(More)
We observe several characteristics of empirical image interpolating algorithms and contribute four novel concepts and claims. First, we interpret well-known classification-based filtering algorithms in terms of their polyphase components. We examine the underlying principles behind the various fixed-scale linear interpolating kernels. Second, we(More)