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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TL;DR: In this article, the authors provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis, and provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging.
Abstract: What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.
Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.
991 citations
TL;DR: This paper indicates how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction, and provides a starting point for people interested in experimenting and contributing to the field of deep learning for medical imaging.
Abstract: What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.
590 citations
Cites background from "Image super-resolution"
...Image super-resolution, reconstructing a higher-resolution image or image sequence from the observed low-resolution image [190], is an exciting application of deep learning methods....
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TL;DR: In this article, a test-time augmentation-based aleatoric uncertainty was proposed to analyze the effect of different transformations of the input image on the segmentation output, and the results showed that the proposed test augmentation provides a better uncertainty estimation than calculating the testtime dropout-based model uncertainty alone and helps to reduce overconfident incorrect predictions.
305 citations
TL;DR: The proposed integrated fusion framework can achieve the integrated fusion of multisource observations to obtain high spatio-temporal-spectral resolution images, without limitations on the number of remote sensing sensors.
Abstract: Remote sensing satellite sensors feature a tradeoff between the spatial, temporal, and spectral resolutions. In this paper, we propose an integrated framework for the spatio–temporal–spectral fusion of remote sensing images. There are two main advantages of the proposed integrated fusion framework: it can accomplish different kinds of fusion tasks, such as multiview spatial fusion, spatio–spectral fusion, and spatio–temporal fusion, based on a single unified model, and it can achieve the integrated fusion of multisource observations to obtain high spatio–temporal–spectral resolution images, without limitations on the number of remote sensing sensors. The proposed integrated fusion framework was comprehensively tested and verified in a variety of image fusion experiments. In the experiments, a number of different remote sensing satellites were utilized, including IKONOS, the Enhanced Thematic Mapper Plus (ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Hyperspectral Digital Imagery Collection Experiment (HYDICE), and Systeme Pour l' Observation de la Terre-5 (SPOT-5). The experimental results confirm the effectiveness of the proposed method.
240 citations
TL;DR: This letter proposes a new single-image super-resolution algorithm named local–global combined networks (LGCNet) for remote sensing images based on the deep CNNs, elaborately designed with its “multifork” structure to learn multilevel representations ofRemote sensing images including both local details and global environmental priors.
Abstract: Super-resolution is an image processing technology that recovers a high-resolution image from a single or sequential low-resolution images Recently deep convolutional neural networks (CNNs) have made a huge breakthrough in many tasks including super-resolution In this letter, we propose a new single-image super-resolution algorithm named local–global combined networks (LGCNet) for remote sensing images based on the deep CNNs Our LGCNet is elaborately designed with its “multifork” structure to learn multilevel representations of remote sensing images including both local details and global environmental priors Experimental results on a public remote sensing data set (UC Merced) demonstrate an overall improvement of both accuracy and visual performance over several state-of-the-art algorithms
203 citations
Cites background from "Image super-resolution"
...Instead of devoting to physical imaging technology, many researchers aim to recover highresolution images from low-resolution ones using an image processing technology called super-resolution [1]....
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References
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TL;DR: A high-resolution fluorescence microscopy method based on high-accuracy localization of photoswitchable fluorophores that can, in principle, reach molecular-scale resolution is developed.
Abstract: We have developed a high-resolution fluorescence microscopy method based on high-accuracy localization of photoswitchable fluorophores. In each imaging cycle, only a fraction of the fluorophores were turned on, allowing their positions to be determined with nanometer accuracy. The fluorophore positions obtained from a series of imaging cycles were used to reconstruct the overall image. We demonstrated an imaging resolution of 20 nm. This technique can, in principle, reach molecular-scale resolution.
7,213 citations
"Image super-resolution" refers methods in this paper
...Moreover, we present an exhaustive summary of the current applications using SR techniques, such as the recent Google Skybox satellite application [16] and unmanned aerial vehicle (UAV) surveillance sequences [17]....
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Book•
01 Jun 1974
TL;DR: Since the lm function provides a lot of features it is rather complicated so it is going to instead use the function lsfit as a model, which computes only the coefficient estimates and the residuals.
Abstract: Since the lm function provides a lot of features it is rather complicated. So we are going to instead use the function lsfit as a model. It computes only the coefficient estimates and the residuals. Now would be a good time to read the help file for lsfit. Note that lsfit supports the fitting of multiple least squares models and weighted least squares. Our function will not, hence we can omit the arguments wt, weights and yname. Also, changing tolerances is a little advanced so we will trust the default values and omit the argument tolerance as well.
6,956 citations
TL;DR: This paper presents a new approach to single-image superresolution, based upon sparse signal representation, which generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods.
Abstract: This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low-resolution and high-resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low-resolution image patch can be applied with the high-resolution image patch dictionary to generate a high-resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs , reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution (SR) and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle SR with noisy inputs in a more unified framework.
4,958 citations
06 Sep 2014
TL;DR: This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
Abstract: We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.
4,445 citations
TL;DR: This paper proposes a “split Bregman” method, which can solve a very broad class of L1-regularized problems, and applies this technique to the Rudin-Osher-Fatemi functional for image denoising and to a compressed sensing problem that arises in magnetic resonance imaging.
Abstract: The class of L1-regularized optimization problems has received much attention recently because of the introduction of “compressed sensing,” which allows images and signals to be reconstructed from small amounts of data. Despite this recent attention, many L1-regularized problems still remain difficult to solve, or require techniques that are very problem-specific. In this paper, we show that Bregman iteration can be used to solve a wide variety of constrained optimization problems. Using this technique, we propose a “split Bregman” method, which can solve a very broad class of L1-regularized problems. We apply this technique to the Rudin-Osher-Fatemi functional for image denoising and to a compressed sensing problem that arises in magnetic resonance imaging.
4,255 citations
"Image super-resolution" refers background or methods in this paper
...related studies have been presented, such as the alternating direction method of multipliers (ADMM) [131,134], the primal–dual (PD) based algorithms [105], the Douglas-Rachford algorithm [135], proximal forward backward splitting (PFBS) [130], and the split-Bregman (SB) method [102]....
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...Thus, − l norm 1 based regularizations are often preferred for their edge-preserving properties [93,101,102]....
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