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Book ChapterDOI

A Novel Approach for Image Super Resolution Using Kernel Methods

30 Jun 2015-pp 126-135
TL;DR: This work applies Kernel Principal Component Analysis with a Gaussian kernel on a patch based data-base constructed from 69 training images up-scaled using bi-cubic interpolation to build an efficient representation and also to learn the regression model.
Abstract: We present a learning based method for image super resolution problem. Our approach uses kernel methods to build an efficient representation and also to learn the regression model. For constructing an efficient set of features, we apply Kernel Principal Component Analysis (Kernel-PCA) with a Gaussian kernel on a patch based data-base constructed from 69 training images up-scaled using bi-cubic interpolation. These features were given as input to a non-linear Support Vector Regression (SVR) model, with Gaussian kernel, to predict the pixels of the high resolution image. The model selection for SVR was performed using grid search. We tested our algorithm on an unseen data-set of 13 images. Our method out-performed a state-of the-art method and achieved an average of 0.92 dB higher Peak signal-to-noise ratio (PSNR). The average improvement in PSNR over bi-cubic interpolation was found to be 3.38 dB.

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Journal ArticleDOI
Yu Liu1, Jie Yang1, Jing Mi1, Jingjing Yang1, Xiao Zhang1 
TL;DR: This paper proposes a novel registration and super-resolution jointed paradigm for medical images under the Internet of thing environment and proposes the novel registration algorithm based on energy feature extraction that achieves the optimal integration of IOT and GPU.
Abstract: This paper proposes a novel registration and super-resolution jointed paradigm for medical images under the Internet of thing environment. In the medical image processing, the matching issue is one catches wide attention with the domain of research. Image registration technique can be divided into similarity measure, optimization, geometric transformation, and interpolation, etc. As the first essential clue of our model, we propose the novel registration algorithm based on energy feature extraction. Generally, the matching energy function by the similarity measurement and a penalty constitution is called the external force and endogenic force separately. The matching is an external force and endogenic force mutual competition, eventually achieves the balanced process. Furtherly, we integrate the game analysis and area feature selection to achieve the better image super-resolution mode through the pretreatment of the image to change the initial value, so as to achieve the purpose of improving the performance. Besides the algorithm level innovation, we integrate the GPU and the IOT to construct the hardware based implementation of the proposed medical image processing system. The latency of registers to read and write data across a GPU’s entire storage system is minimal, it is private to each thread, and can only be accessed by its owning thread. For each thread, the local memory is also private and it is often used to deal with the problem of overflow register, reducing the buffer overflow caused by the entire application of a substantial decline in the possibility and shared memory is visible to all threads within the thread block. We then achieve the optimal integration of IOT and GPU. The experimental result proves the robustness of the method.
References
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Journal ArticleDOI
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

40,826 citations


"A Novel Approach for Image Super Re..." refers methods in this paper

  • ...We used LIBSVM for non linear SVR training [14]....

    [...]

Book
Vladimir Vapnik1
01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

40,147 citations


"A Novel Approach for Image Super Re..." refers methods in this paper

  • ...In the next two subsections we give a brief introduction to kernel PCA and Support Vector Regression (SVR) [10,11] respectively....

    [...]

  • ...In this work we combine two such algorithms, Kernel Principal Component Analysis [9] (kernel PCA) and Support Vector Regression (SVR) to solve the problem of single-image super-resolution....

    [...]

  • ...Support Vector Regression [10,13] was developed as an extension of Support Vector Machines [11], which are one of the most powerful algorithms in machine learning and have excellent generalization ability....

    [...]

  • ...However, we have applied it on LR image patches in order to extract a powerful set of features which are eventually fed to yet another kernel based method (Support Vector Regression)....

    [...]

Journal ArticleDOI
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

37,861 citations


"A Novel Approach for Image Super Re..." refers methods in this paper

  • ...In the next two subsections we give a brief introduction to kernel PCA and Support Vector Regression (SVR) [10,11] respectively....

    [...]

  • ...Support Vector Regression [10,13] was developed as an extension of Support Vector Machines [11], which are one of the most powerful algorithms in machine learning and have excellent generalization ability....

    [...]

Journal ArticleDOI
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Abstract: In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

10,696 citations


"A Novel Approach for Image Super Re..." refers methods in this paper

  • ...In the next two subsections we give a brief introduction to kernel PCA and Support Vector Regression (SVR) [10,11] respectively....

    [...]

  • ...In this work we combine two such algorithms, Kernel Principal Component Analysis [9] (kernel PCA) and Support Vector Regression (SVR) to solve the problem of single-image super-resolution....

    [...]

  • ...Support Vector Regression [10,13] was developed as an extension of Support Vector Machines [11], which are one of the most powerful algorithms in machine learning and have excellent generalization ability....

    [...]

  • ...However, we have applied it on LR image patches in order to extract a powerful set of features which are eventually fed to yet another kernel based method (Support Vector Regression)....

    [...]

BookDOI
01 Dec 2001
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
Abstract: From the Publisher: In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

7,880 citations