Results 1 - 10
of
92
Fast and Robust Multi-Frame Super-Resolution
- IEEE Transactions on Image ProcessinG
, 2003
"... In the last two decades, many papers have been published, proposing a variety of methods for multi- frame resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses th ..."
Abstract
-
Cited by 272 (37 self)
- Add to MetaCart
(Show Context)
In the last two decades, many papers have been published, proposing a variety of methods for multi- frame resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate approach using L norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation, and results in images with sharp edges.
Guided Image Filtering
"... Abstract. In this paper, we propose a novel type of explicit image filter- guided filter. Derived from a local linear model, the guided filter generates the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided fil ..."
Abstract
-
Cited by 145 (1 self)
- Add to MetaCart
(Show Context)
Abstract. In this paper, we propose a novel type of explicit image filter- guided filter. Derived from a local linear model, the guided filter generates the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can perform as an edge-preserving smoothing operator like the popular bilateral filter [1], but has better behavior near the edges. It also has a theoretical connection with the matting Laplacian matrix [2], so is a more generic concept than a smoothing operator and can better utilize the structures in the guidance image. Moreover, the guidedfilterhasafastandnon-approximatelinear-time algorithm, whose computational complexity is independent of the filtering kernel size. We demonstrate that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling. 1
A Closed-Form Solution to Natural Image Matting
- IEEE TRANS. PAMI
, 2008
"... Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed—at each pixel we must estim ..."
Abstract
-
Cited by 136 (2 self)
- Add to MetaCart
Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed—at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity (“alpha matte”) from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation. In this paper, we present a closed-form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors and show that in the resulting expression, it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed-form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high-quality mattes for natural images may be obtained from a small amount of user input.
A closed form solution to natural image matting
- Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
"... Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed — at each pixel we must esti ..."
Abstract
-
Cited by 110 (3 self)
- Add to MetaCart
(Show Context)
Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed — at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity (“alpha matte”) from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation. In this paper we present a closed form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors, and show that in the resulting expression it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high quality mattes can be obtained on natural images from a small amount of user input. 1.
A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution
, 2006
"... Super-resolution algorithms reconstruct a high-resolution image from a set of low-resolution images of a scene. Precise alignment of the input images is an essential part of such algorithms. If the low-resolution images are undersampled and have aliasing artifacts, the performance of standard regist ..."
Abstract
-
Cited by 104 (9 self)
- Add to MetaCart
Super-resolution algorithms reconstruct a high-resolution image from a set of low-resolution images of a scene. Precise alignment of the input images is an essential part of such algorithms. If the low-resolution images are undersampled and have aliasing artifacts, the performance of standard registration algorithms decreases. We propose a frequency domain technique to precisely register a set of aliased images, based on their low-frequency, aliasing-free part. A high-resolution image is then reconstructed using cubic interpolation. Our algorithm is compared to other algorithms in simulations and practical experiments using real aliased images. Both show very good visual results and prove the attractivity of our approach in the case of aliased input images. A possible application is to digital cameras where a set of rapidly acquired images can be used to recover a higher-resolution final image.
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 ..."
Abstract
-
Cited by 101 (16 self)
- Add to MetaCart
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.
Multi-frame demosaicing and super-resolution of color images
- IEEE Trans. on Image Processing
, 2006
"... In the last two decades, two related categories of problems have been studied independently in the image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and as conventional color digital cameras suffer from both low-spatial ..."
Abstract
-
Cited by 53 (8 self)
- Add to MetaCart
(Show Context)
In the last two decades, two related categories of problems have been studied independently in the image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a MAP estimation technique by minimizing a multi-term cost function. The L 1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of
Super-resolution reconstruction of hyperspectral images
- IEEE Trans. on Image Proc
, 2005
"... Abstract—Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of thes ..."
Abstract
-
Cited by 39 (0 self)
- Add to MetaCart
(Show Context)
Abstract—Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images. Improving their resolution has a high payoff, but applying superresolution techniques separately to every spectral band is problematic for two main reasons. First, the number of spectral bands can be in the hundreds, which increases the computational load excessively. Second, considering the bands separately does not make use of the information that is present across them. Furthermore, separate band super resolution does not make use of the inherent low dimensionality of the spectral data, which can effectively be used to improve the robustness against noise. In this paper, we introduce a novel super-resolution method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then, a method for applying super resolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene as a combination of a small number of spectral basis functions. Index Terms—Hyperspectral, image reconstruction, information fusion, resolution enhancement, spectral, super resolution.
Robust fusion of irregularly sampled data using adaptive normalized convolution
- EURASIP Journal on Applied Signal Processing
, 2006
"... We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to ..."
Abstract
-
Cited by 38 (5 self)
- Add to MetaCart
(Show Context)
We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signal-to-noise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of super-resolution image reconstruction. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved. 1.
Super-resolution without Explicit Subpixel Motion Estimation
"... The need for precise (subpixel accuracy) motion estimates in conventional super-resolution has limited its applicability to only video sequences with relatively simple motions such as global translational or affine displacements. In this paper, we introduce a novel framework for adaptive enhancement ..."
Abstract
-
Cited by 32 (9 self)
- Add to MetaCart
(Show Context)
The need for precise (subpixel accuracy) motion estimates in conventional super-resolution has limited its applicability to only video sequences with relatively simple motions such as global translational or affine displacements. In this paper, we introduce a novel framework for adaptive enhancement and spatio-temporal upscaling of videos containing complex activities without explicit need for accurate motion estimation. Our approach is based on multidimensional kernel regression, where each pixel in the video sequence is approximated with a 3-D local (Taylor) series, capturing the essential local behavior of its spatiotemporal neighborhood. The coefficients of this series are estimated by solving a local weighted least-squares problem, where the weights are a function of the 3-D space-time orientation in the neighborhood. As this framework is fundamentally based upon the comparison of neighboring pixels in both space and time, it implicitly contains information about the local motion of the pixels across time, therefore rendering unnecessary an explicit computation of motions of modest size. The proposed approach not only significantly widens the applicability of superresolution methods to a broad variety of video sequences containing complex motions, but also yields improved overall performance. Using several examples, we illustrate that the developed algorithm has super-resolution capabilities that provide
