Image super-resolution
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.
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01 Jan 2020
TL;DR: Different techniques related to SR are classified into two main domains like frequency and spatial, which helps to the new researchers to get idea about SR concept and techniques with their limitations.
Abstract: This paper gives detailed review of techniques for super resolution (SR) which is growing research area of image processing. Super resolution is nothing but upgrading resolution of image or video using number of low resolution images or frames. Spatial and temporal enhancements where SR concept is preliminary applied. Different techniques related to SR are classified into two main domains like frequency and spatial are summarized in this paper. The new method with neural network based super-resolution is emerged due to its fast and parallel computation of data. This paper helps to the new researchers to get idea about SR concept and techniques with their limitations.
5 citations
TL;DR: A spatially weighted super-resolution (SR) algorithm, which takes into consideration the distribution of every information that characterise different image areas, which is more efficient and easier to implement and preserves well the smooth regions of the image and also sharp edges.
Abstract: Here, the authors propose a spatially weighted super-resolution (SR) algorithm, which takes into consideration the distribution of every information that characterise different image areas. The authors investigate to use a combined spatially weighted regularisation of the bilateral total variation and a second-order term increasing then the robustness of the proposed SR approach with respect to blur and noise degradations. In addition, the authors propose an iterative Bregman iteration algorithm to resolve the obtained optimisation SR problem. As a result, this regularisation is more efficient and easier to implement; moreover, it preserves well the smooth regions of the image and also sharp edges. Using different simulated and real tests, the authors prove the efficiency of the proposed algorithm compared to some SR methods.
5 citations
27 Jun 2018
TL;DR: An improved algorithm using Fourier transform and zero-padding resampling instead of bicubic interpolation for frequency domain interpolation is proposed and is superior to the traditional two-dimensional wavelet reconstruction algorithm, which can be applied to the single-frame remote sensing image super-resolution reconstruction.
Abstract: The obtained precisely high frequency information is the key of single-frame image super-resolution reconstruction by using two-dimensional wavelet. Because the bicubic interpolation of high frequency components decomposed by wavelet will introduce noise, it will affect reconstruction effect. An improved algorithm using Fourier transform and zero-padding resampling instead of bicubic interpolation is proposed in this paper. The advantage of frequency domain interpolation is obtained by using Fourier transform and zero-padding resampling. And high frequency components obtained by wavelet decomposition of the original image can be interpolated optimally without introducing noise, which makes the high frequency details more precise in the reconstruction process. The experimental results show that the improved algorithm is superior to the traditional two-dimensional wavelet reconstruction algorithm, which can be applied to the single-frame remote sensing image super-resolution reconstruction.
4 citations
Cites background from "Image super-resolution"
...The single-frame remote sensing (RS) image superresolution (SR) reconstruction is the process of estimating a high-resolution image by a low-resolution image [1-4]....
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19 Feb 2020
TL;DR: Results of the application of enhanced deep residual networks for single image super-resolution (EDSR) on retinal fundus images, based on the SRResNet architecture involving skip connections are presented.
Abstract: Fundus imaging is widely used for the diagnosis of retinal diseases. Major ophthalmic diseases like glaucoma, diabetic retinopathy (DR), age-related macular degeneration (AMD) are diagnosed by examining retinal fundus images. Therefore, the efficient and reliable diagnosis largely depends upon the resolution of the images. In different diseased conditions, different pathologies and landmarks (haemorrhages, microaneurysms, exudates, blood vessels, optic disc and optic cup, fovea) of the retina get affected. In clinical situations it is often not possible to obtain good high-resolution images. Here, the techniques of super-resolution can be applied. The objective of super-resolution is to obtain a high-resolution image from a low-resolution input image. In this paper, we present results of the application of enhanced deep residual networks for single image super-resolution (EDSR) on retinal fundus images. This network is based on the SRResNet architecture involving skip connections. Using the public RIGA dataset, which consists of glaucoma and normal fundus images, we have trained the model using 2x, 4x and 8x scaling with three different optimizers each (namely ADAM, Stochastic Gradient Descent and RMSprop) to determine which optimizer is best for the different scales. We have also provided results obtained by varying the residual blocks in the network.
4 citations
Cites background from "Image super-resolution"
...Super Resolution(SR) refers to the procedure of getting high resolution (HR) images from low resolution (LR) images which has numerous applications in the fields of medical imaging, surveillance, biometric information enhancement etc.(1) A good introductory review on super-resolution and compressive sensing is given by Lakshminarayanan et al....
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TL;DR: In this paper, the theory of visual keywords is applied to product appearance detection for the first time, which provides a reference for further research.
4 citations
References
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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 2011TL;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
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]....
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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....
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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]....
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