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: The sensor design contains thirteen different variants of the 4T pixel architecture to investigate the effects of changing its core parameters, which can be optimised for scientific applications where detection of small amounts of charge is required.
Abstract: This paper presents the design and characterisation of FORTIS (4T Test Image Sensor), which is a low noise, CMOS monolithic active pixel sensor for scientific applications. The pixels present in FORTIS are based around the four transistor (4T) pixel architecture, which is already widely used in the commercial imaging community. The sensor design contains thirteen different variants of the 4T pixel architecture to investigate the effects of changing its core parameters. The variants include differences in the pixel pitch, the diode size, the in-pixel source follower, and the capacitance of the floating diffusion node (the input node of the in-pixel source follower). Processing variations have also been studied, which include varying the resistivity of the epitaxial layer and investigating the effects of a special deep p-well layer. By varying these parameters, the 4T pixel architecture can be optimised for scientific applications where detection of small amounts of charge is required.
25 citations
03 Dec 2010
TL;DR: An effective convergence criterion is proposed, which is able to terminate the iterative L 1 and L2
Abstract: A hybrid error model with L 1 and L 2 norm minimization criteria is proposed in this paper for image/video super-resolution. A membership function is defined to adaptively control the tradeoff between the L 1 and L 2 norm terms. Therefore, the proposed hybrid model can have the advantages of both L 1 norm minimization (i.e. edge preservation) and L 2 norm minimization (i.e. smoothing noise). In addition, an effective convergence criterion is proposed, which is able to terminate the iterative L 1 and L 2 norm minimization process efficiently. Experimental results on images corrupted with various types of noises demonstrate the robustness of the proposed algorithm and its superiority to representative algorithms.
24 citations
TL;DR: Extensive experimental results prove that this method cannot only remove noise from the corrupted image well, but also preserve more details and textures than some state-of-the-art methods.
23 citations
"Image super-resolution" refers background in this paper
...Ok is the operator excluding the unobservable pixels from the kth image [47,74,75]....
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TL;DR: A robust Bayesian SR algorithm is developed, which is able to produce high-quality deblurred results, which show a suppressing of high-frequency artifacts and less ringing artifacts, with a higher peak signal-to-noise ratio (PSNR).
Abstract: This paper presents a robust algorithm to recover high-frequency information from compressed low-resolution (LR) video sequences. Previous super-resolution (SR) approaches have succeeded in resolution enhancement when the motion in the LR sequence is simple. However, when the motion is complex, new artifacts will be introduced in the SR processing. To solve this problem, we develop a robust Bayesian SR algorithm with two steps. We first isolate the frames individually to get their corresponding initial SR solutions within the Bayesian framework. Secondly, with a robust cost function to reject outliers and noise, final SR images are achieved with multiple LR frames. In the mean time, we impose the constraint that the distribution of high-resolution (HR) image gradient should be equal to one of the corresponding decompressed LR images to sharpen the edges of the results. As a result of these steps, we are able to produce high-quality deblurred results, which show a suppressing of high-frequency artifacts and less ringing artifacts, with a higher peak signal-to-noise ratio (PSNR).
19 citations
"Image super-resolution" refers background in this paper
...Compressed video SR has also been a focus [185,186]....
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25 Aug 2006
TL;DR: In this article, a multi-lens imaging system was proposed for iris recognition, and the authors investigated the use of a novel multilens image system in the context of biometric identification.
Abstract: We investigate the use of a novel multi-lens imaging system in the context of biometric identification, and more
specifically, for iris recognition. Multi-lenslet cameras offer a number of significant advantages over standard
single-lens camera systems, including thin form-factor and wide angle of view. By using appropriate lenslet spacing
relative to the detector pixel pitch, the resulting ensemble of images implicitly contains subject information
at higher spatial frequencies than those present in a single image. Additionally, a multi-lenslet approach enables
the use of observational diversity, including phase, polarization, neutral density, and wavelength diversities. For
example, post-processing multiple observations taken with differing neutral density filters yields an image having
an extended dynamic range. Our research group has developed several multi-lens camera prototypes for the
investigation of such diversities.
In this paper, we present techniques for computing a high-resolution reconstructed image from an ensemble of
low-resolution images containing sub-pixel level displacements. The quality of a reconstructed image is measured
by computing the Hamming distance between the Daugman 4 iris code of a conventional reference iris image,
and the iris code of a corresponding reconstructed image. We present numerical results concerning the effect of
noise and defocus blur in the reconstruction process using simulated data and report preliminary work on the
reconstruction of actual iris data obtained with our camera prototypes.
19 citations