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Journal ArticleDOI

Image super-resolution

Linwei Yue1, Huanfeng Shen1, Jie Li1, Qiangqiang Yuan1, Hongyan Zhang1, Liangpei Zhang1 
01 Nov 2016-Signal Processing (Elsevier)-Vol. 128, pp 389-408
TL;DR: This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years, and discusses the current obstacles for future research.
About: This article is published in Signal Processing.The article was published on 2016-11-01. It has received 378 citations till now.
Citations
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Journal ArticleDOI
TL;DR: This study presents a novel sparse-representation based multi-frame super-resolution technique to reconstruct a high-resolution frame from multiple noisy low-resolution frames by using registration in sub-pixel accuracy and adaptive weighted feature operators.
Abstract: This study presents a novel sparse-representation based multi-frame super-resolution (SR) technique to reconstruct a high-resolution (HR) frame from multiple noisy low-resolution (LR) frames by using registration in sub-pixel accuracy and adaptive weighted feature operators. First, the registration of multiple frames in sub-pixel level and the mapping of pixels from LR frames to HR grid puts more information into the reconstructed image with respect to the conventional sparse representation based single image SR technique. This improves the overall resolution of the output image. Second, the introduction of adaptive weighted feature operators in the reconstruction process has significantly improved the robustness of the algorithm to noisy input frames. Hence, from the outputs, it can be seen that the proposed method outperforms the recent techniques in terms of noise robustness even in the higher noise level in the input image. The performance of the proposed algorithm is evaluated and quantified through a set of well-defined quality metrics and compared with some recently developed techniques. The results of the proposed technique confirm the claims of the authors.

8 citations

Journal ArticleDOI
TL;DR: Experimental results on a variety of MR images indicate improvement in the quality of the high-resolution MR image, in terms of qualitative and quantitative criteria.
Abstract: In this paper, a method is proposed to achieve a high-resolution image from a low-resolution image. Because of the ill-posedness of the super-resolution problem, sparsity constraint is used as a prior, in this work. On the one hand, we use convolutional sparse representation on the whole image different from the patch-based method. On the other hand, we apply fewer filters even in smaller sizes for reconstructing the high-resolution image. Therefore, despite the reduced processing time, the reconstructed image quality is improved compared to the reference methods. In this work, the training images are different in terms of content from the testing images. Experimental results on a variety of MR images indicate improvement in the quality of the high-resolution MR image, in terms of qualitative and quantitative criteria.

8 citations

Journal ArticleDOI
TL;DR: In this article, it is demonstrated that alterations in the localised dielectric characteristics of inkjet-printed electronics can be qualitatively distinguished using quasi-optically-based, sub-THz reflection spectroscopy.

8 citations

Journal ArticleDOI
TL;DR: The proposed interpolation-based single-frame image super-resolution approach using orthogonal rotation invariant moments (ORIMs) is shown to be very fast as compared to NLM, NLM+ZMs andNLM+PZMs-based methods.

8 citations

01 Jan 2003
TL;DR: A regularized iterative reconstruction algorithm adopted to overcome the ill-posedness problem resulting from inaccurate subpixel registration suitable for applications with multiframe environments.
Abstract: In this paper, we propose a high-resolution image reconstruction algorithm considering inaccurate subpixel registration. A regularized iterative reconstruction algorithm is adopted to overcome the ill-posedness problem resulting from inaccurate subpixel registration. In particular, we use multichannel image reconstruction algorithms suitable for applications with multiframe environments. Since the registration error in each low-resolution image has a different pattern, the regularization parameters are determined adaptively for each channel. We propose two methods for estimating the regularization parameter automatically. The proposed algorithms are robust against registration error noise, and they do not require any prior information about the original image or the registration error process. Information needed to determine the regularization parameter and to reconstruct the image is updated at each iteration step based on the available partially reconstructed image. Experimental results indicate that the proposed algorithms outperform conventional approaches in terms of both objective measurements and visual evaluation.

7 citations

References
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Journal ArticleDOI
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

40,609 citations

Book
23 May 2011
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Abstract: Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas–Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for l1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. We also discuss general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations.

17,433 citations

Journal ArticleDOI
TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Abstract: Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together.The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the

16,538 citations


"Image super-resolution" refers background in this paper

  • ...As the l2 norm represents a smoothing prior and the l1 norm tends to preserve the edges, the lp ( ≤ ≤ p 1 2) norm achieves a balance between them, thereby avoiding the staircase effect [110]....

    [...]

Journal ArticleDOI
TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.

15,225 citations


"Image super-resolution" refers background in this paper

  • ...[93,103], based on the fact that an image is naturally “blocky” and discontinuous....

    [...]

Book
01 Jan 1977

8,009 citations


"Image super-resolution" refers background in this paper

  • ...In the early years, the smoothness of natural images was mainly considered, which leads to the quadratic property of the regularizations [99,100]....

    [...]