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Super-Resolution from a Single Image
"... Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches fr ..."
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Cited by 139 (5 self)
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Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database). In this paper we propose a unified framework for combining these two families of methods. We further show how this combined approach can be applied to obtain super resolution from as little as a single image (with no database or prior examples). Our approach is based on the observation that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Recurrence of patches within the same image scale (at subpixel misalignments) gives rise to the classical super-resolution, whereas recurrence of patches across different scales of the same image gives rise to example-based super-resolution. Our approach attempts to recover at each pixel its best possible resolution increase based on its patch redundancy within and across scales. 1.
Advances and Challenges in Super-Resolution
- International Journal of Imaging Systems and Technology
, 2004
"... Super-resolution reconstruction produces one or a set of high-resolution images from a sequence of low-resolution frames. This paper reviews a variety of super-resolution methods proposed in the last twenty years, and provides some insight to, and a summary of, our recent contributions to the gen ..."
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Cited by 101 (16 self)
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Super-resolution reconstruction produces one or a set of high-resolution images from a sequence of low-resolution frames. This paper reviews a variety of super-resolution methods proposed in the last twenty years, and provides some insight to, and a summary of, our recent contributions to the general super-resolution problem. In the process, a detailed study of several very important aspects of super-resolution, often ignored in the literature, is presented. Specifically, we discuss robustness, treatment of color, and dynamic operation modes. Novel methods for addressing these issues are accompanied by experimental results on simulated and real data. Finally, some future challenges in super-resolution are outlined and discussed.
Generalizing the non-local-means to super-resolution reconstruction
- IN IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2009
"... Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inacc ..."
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Cited by 81 (4 self)
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Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inaccurately, as often happens for nonglobal motion fields, annoying artifacts appear in the super-resolved outcome. Encouraged by recent developments on the video denoising problem, where state-of-the-art algorithms are formed with no explicit motion estimation, we seek a super-resolution algorithm of similar nature that will allow processing sequences with general motion patterns. In this paper, we base our solution on the Nonlocal-Means (NLM) algorithm. We show how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation. Results on several test movies show that the proposed method is very successful in providing super-resolution on general sequences.
Image super-resolution using gradient profile prior
, 2008
"... In this paper, we propose an image super-resolution approach using a novel generic image prior – gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can p ..."
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Cited by 70 (4 self)
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In this paper, we propose an image super-resolution approach using a novel generic image prior – gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hiresolution image is sharp while has rare ringing or jaggy artifacts.
Space-time super-resolution
- PAMI
, 2005
"... We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple low-resolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By âtemporal super-resolutionâ we mean recoverin ..."
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Cited by 65 (2 self)
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We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple low-resolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By âtemporal super-resolutionâ we mean recovering rapid dynamic events that occur faster than regular frame-rate. Such dynamic events are not visible (or else observed incorrectly) in any of the input sequences, even if these are played in âslow-motionâ. The spatial and temporal dimensions are very different in nature, yet are interrelated. This leads to interesting visual tradeoffs in time and space, and to new video applications. These include: (i) treatment of spatial artifacts (e.g., motionblur) by increasing the temporal resolution, and (ii) combination of input sequences of different space-time resolutions (e.g., NTSC, PAL, and even high quality still images) to generate a high quality video sequence. We further analyze and compare characteristics of temporal super-resolution to those of spatial super-resolution. These include: How many video cameras are needed to obtain increased resolution? What is the upper bound on resolution improvement via super-resolution? What is the optimal camera configuration for various scenarios? What is the temporal analogue to the spatial âringingâ effect?
Computer Vision Applied to Super-Resolution”,
- IEEE Signal Processing Magazine,
, 2003
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Is Denoising Dead?
, 2010
"... Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable. A pertin ..."
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Cited by 49 (13 self)
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Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable. A pertinent question then to ask is whether there is a theoretical limit to denoising performance and, more importantly, are we there yet? As camera manufacturers continue to pack increasing numbers of pixels per unit area, an increase in noise sensitivity manifests itself in the form of a noisier image. We study the performance bounds for the image denoising problem. Our work in this paper estimates a lower bound on the mean squared error of the denoised result and compares the performance of current state-of-the-art denoising methods with this bound. We show that despite the phenomenal recent progress in the quality of denoising algorithms, some room for improvement still remains for a wide class of general images, and at certain signal-to-noise levels. Therefore, image denoising is not dead—yet.
Statistical Performance Analysis of Superresolution Image Reconstruction
, 2004
"... Recently, there has been much work developing super-resolution algorithms for combining a set of low quality images to produce a set of higher quality images. In most cases, such algorithms must first register the collection of images to a common sampling grid and then reconstruct the high resolutio ..."
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Cited by 39 (9 self)
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Recently, there has been much work developing super-resolution algorithms for combining a set of low quality images to produce a set of higher quality images. In most cases, such algorithms must first register the collection of images to a common sampling grid and then reconstruct the high resolution image. While many such algorithms have been proposed to address each one of these subproblems, no work has addressed the overall performance limits for this joint estimation problem. In this paper, we analyze the performance limits from statistical first principles using the Cramer-Rao bound. We offer insight into the fundamental bottlenecks limiting the performance of multiframe image reconstruction algorithms and hence super-resolution.
Image and Video Upscaling from Local Self-Examples
"... We propose a new high-quality and efficient single-image upscaling technique that extends existing example-based super-resolution frameworks. In our approach we do not rely on an external example database or use the whole input image as a source for example patches. Instead, we follow a local self-s ..."
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Cited by 39 (0 self)
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We propose a new high-quality and efficient single-image upscaling technique that extends existing example-based super-resolution frameworks. In our approach we do not rely on an external example database or use the whole input image as a source for example patches. Instead, we follow a local self-similarity assumption on natural images and extract patches from extremely localized regions in the input image. This allows us to reduce considerably the nearest-patch search time without compromising quality in most images. Tests, that we perform and report, show that the local-self similarity assumption holds better for small scaling factors where there are more example patches of greater relevance. We implement these small scalings using dedicated novel non-dyadic filter banks, that we derive based on principles that model the upscaling process. Moreover, the new filters are nearly-biorthogonal and hence produce high-resolution images that are highly consistent with the input image without solving implicit back-projection equations. The local and explicit nature of our algorithm makes it simple, efficient and allows a trivial parallel implementation on a GPU. We demonstrate the new method ability to produce high-quality resolution enhancement, its application to video sequences with no algorithmic modification, and its efficiency to perform real-time enhancement of lowresolution video standard into recent high-definition formats.
Coded Strobing Photography: Compressive Sensing of High-speed Periodic Events
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
"... We show that, via temporal modulation, one can observe a high-speed periodic event well beyond the abilities of a low-frame rate camera. By strobing the exposure with unique sequences within the integration time of each frame, we take coded projections of dynamic events. From a sequence of such fra ..."
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Cited by 35 (6 self)
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We show that, via temporal modulation, one can observe a high-speed periodic event well beyond the abilities of a low-frame rate camera. By strobing the exposure with unique sequences within the integration time of each frame, we take coded projections of dynamic events. From a sequence of such frames, we reconstruct a high-speed video of the high frequency periodic process. Strobing is used in entertainment, medical imaging and industrial inspection to generate lower beat frequencies. But this is limited to scenes with a detectable single dominant frequency and requires high-intensity lighting. In this paper, we address the problem of sub-Nyquist sampling of periodic signals and show designs to capture and reconstruct such signals. The key result is that for such signals the Nyquist rate constraint can be imposed on strobe-rate rather than the sensorrate. The technique is based on intentional aliasing of the frequency components of the periodic signal while the reconstruction algorithm exploits recent advances in sparse representations and compressive sensing. We exploit the sparsity of periodic signals in Fourier domain to develop reconstruction algorithms that are inspired by compressive sensing.
