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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 69 (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.
Iterative kernel principal component analysis for image modeling
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Abstract- In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limite ..."
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Cited by 57 (3 self)
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Abstract- In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution and denoising performance are comparable to existing methods.
Image deblurring and superresolution by adaptive sparse domain selection and adaptive regularization
- IEEE Trans. Image Process
, 2011
"... Abstract—As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of the-norm optimization techniques and the fact that natural images are intrinsically ..."
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Cited by 56 (11 self)
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Abstract—As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of the-norm optimization techniques and the fact that natural images are intrinsically sparse in some do-mains. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a precollected dataset of ex-ample image patches, and then, for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of ex-ample image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image nonlocal self-similarity is introduced as an-other regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain se-lection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception. Index Terms—Deblurring, image restoration (IR), regulariza-tion, sparse representation, super-resolution. I.
Non-local Regularization of Inverse Problems
, 2008
"... This article proposes a new framework to regularize linear inverse problems using the total variation on non-local graphs. This nonlocal graph allows to adapt the penalization to the geometry of the underlying function to recover. A fast algorithm computes iteratively both the solution of the regul ..."
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Cited by 56 (3 self)
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This article proposes a new framework to regularize linear inverse problems using the total variation on non-local graphs. This nonlocal graph allows to adapt the penalization to the geometry of the underlying function to recover. A fast algorithm computes iteratively both the solution of the regularization process and the non-local graph adapted to this solution. We show numerical applications of this method to the resolution of image processing inverse problems such as inpainting, super-resolution and compressive sampling.
Video epitomes
- in Proc. IEEE Conf. Comput. Vis. Pattern Recog
, 2005
"... Recently, “epitomes ” were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. In this paper, we describe how epitomes can be used to model video data and we describe significant computational speedups that can ..."
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Cited by 54 (0 self)
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Recently, “epitomes ” were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. In this paper, we describe how epitomes can be used to model video data and we describe significant computational speedups that can be incorporated into the epitome inference and learning algorithm. In the case of videos, epitomes are estimated so as to model most of the small space-time cubes from the input data. Then, the epitome can be used for various modeling and reconstruction tasks, of which we show results for video super-resolution, video interpolation, and object removal. Besides computational efficiency, an interesting advantage of the epitome as a representation is that it can be reliably estimated even from videos with large amounts of missing data. We illustrate this ability on the task of reconstructing the dropped frames in video broadcast using only the degraded video and also in denoising a severely corrupted video. 1
Exploiting the Sparse Derivative Prior for Super-Resolution and Image Demosaicing
- In IEEE Workshop on Statistical and Computational Theories of Vision
, 2003
"... When a band-pass filter is applied to a natural image, the distribution of the output has a consistent, distinctive form across many different images, with the distribution sharply peaked at zero and relatively heavy-tailed. This prior has been exploited for several image processing tasks. We show h ..."
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Cited by 50 (1 self)
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When a band-pass filter is applied to a natural image, the distribution of the output has a consistent, distinctive form across many different images, with the distribution sharply peaked at zero and relatively heavy-tailed. This prior has been exploited for several image processing tasks. We show how this prior on the appearance of natural images can also be used to estimate full-resolution images from incomplete data. The unobserved image pixels are modeled with a factor graph. The constraints in the factor graph are based on the characteristic distribution of image derivatives. We introduce an efficient representation for finding candidate values for patches of the image being estimated, avoiding combinatorial explosion. The usefulness of this approach is demonstrated by applying it to two applications: extracting a high-resolution image from a low-resolution version and estimating a full-color image from an image with one color sample per pixel. We show how the super resolution system produces noticeably sharper images, with few significant artifacts. The demosaicing system produces full-color images with fewer color-fringing artifacts than images from other methods.
Using Photographs to Enhance Videos of a Static Scene
- EUROGRAPHICS SYMPOSIUM ON RENDERING (2007) JAN KAUTZ AND SUMANTA PATTANAIK (EDITORS)
, 2007
"... We present a framework for automatically enhancing videos of a static scene using a few photographs of the same scene. For example, our system can transfer photographic qualities such as high resolution, high dynamic range and better lighting from the photographs to the video. Additionally, the user ..."
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Cited by 49 (6 self)
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We present a framework for automatically enhancing videos of a static scene using a few photographs of the same scene. For example, our system can transfer photographic qualities such as high resolution, high dynamic range and better lighting from the photographs to the video. Additionally, the user can quickly modify the video by editing only a few still images of the scene. Finally, our system allows a user to remove unwanted objects and camera shake from the video. These capabilities are enabled by two technical contributions presented in this paper. First, we make several improvements to a state-of-the-art multiview stereo algorithm in order to compute view-dependent depths using video, photographs, and structure-from-motion data. Second, we present a novel image-based rendering algorithm that can re-render the input video using the appearance of the photographs while preserving certain temporal dynamics such as specularities and dynamic scene lighting.
Data-driven Visual Similarity for Cross-domain Image Matching
"... Figure 1: In this paper, we are interested in defining visual similarity between images across different domains, such as photos taken in different seasons, paintings, sketches, etc. What makes this challenging is that the visual content is only similar on the higher scene level, but quite dissimila ..."
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Cited by 42 (5 self)
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Figure 1: In this paper, we are interested in defining visual similarity between images across different domains, such as photos taken in different seasons, paintings, sketches, etc. What makes this challenging is that the visual content is only similar on the higher scene level, but quite dissimilar on the pixel level. Here we present an approach that works well across different visual domains. The goal of this work is to find visually similar images even if they appear quite different at the raw pixel level. This task is particularly important for matching images across visual domains, such as photos taken over different seasons or lighting conditions, paintings, hand-drawn sketches, etc. We propose a surprisingly simple method that estimates the relative importance of different features in a query image based on the notion of “data-driven uniqueness”. We employ standard tools from discriminative object detection in a novel way, yielding a generic approach that does not depend on a particular image representation or a specific visual domain. Our approach shows good performance on a number of difficult crossdomain visual tasks e.g., matching paintings or sketches to real photographs. The method also allows us to demonstrate novel applications such as Internet re-photography, and painting2gps. While at present the technique is too computationally intensive to be practical for interactive image retrieval, we hope that some of the ideas will eventually become applicable to that domain as well.
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.
Super-resolution Enhancement of Video
- In Proc. Artificial Intelligence and Statistics
, 2003
"... We consider the problem of enhancing the resolution of video through the addition of perceptually plausible high frequency information. ..."
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Cited by 38 (0 self)
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We consider the problem of enhancing the resolution of video through the addition of perceptually plausible high frequency information.
