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20
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.
Interactive segmentation for manipulation in unstructured environments
- In IROS
, 2008
"... Abstract — To perform successful manipulation, robots depend on information about objects in their environment. In unstructured environments, such information cannot be given to the robot a priori. It is thus critical for the robot to be able to continuously acquire task-specific information about o ..."
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Cited by 32 (3 self)
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Abstract — To perform successful manipulation, robots depend on information about objects in their environment. In unstructured environments, such information cannot be given to the robot a priori. It is thus critical for the robot to be able to continuously acquire task-specific information about objects. Towards this goal, we present a robust perceptual skill for identifying, tracking, and segmenting objects in a cluttered environment. We increase the robot’s perceptual capabilities by closely coupling them with the robot’s manipulation skills. The robot’s interaction with objects in the environment creates a perceptual signal, i.e. motion, that renders segmentation and tracking robust and reliable. In addition, the resulting perceptual signal reveals the type of segmentation most relevant to manipulation, namely a segmentation of rigidly connected physical bodies. We demonstrate our approach with experiments on a real world mobile manipulation platform with multiple objects in a cluttered scene. I.
Super Resolution With Probabilistic Motion Estimation
"... Abstract—Super-resolution reconstruction (SRR) has long been relying on very accurate motion estimation between the frames for a successful process. However, recent works propose SRR that bypasses the need for an explicit motion estimation [11], [15]. In this correspondence, we present a new framewo ..."
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Cited by 12 (0 self)
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Abstract—Super-resolution reconstruction (SRR) has long been relying on very accurate motion estimation between the frames for a successful process. However, recent works propose SRR that bypasses the need for an explicit motion estimation [11], [15]. In this correspondence, we present a new framework that ultimately leads to the same algorithm as in our prior work [11]. The contribution of this paper is two-fold. First, the suggested approach is much simpler and more intuitive, relying on the classic SRR formulation, and using a probabilistic and crude motion estimation. Second, the new approach offers various extensions not covered in our previous work, such as more general re-sampling tasks (e.g., de-interlacing). Index Terms—Deinterlacing, probabilistic motion estimation, super resolution. I.
Variational Bayesian Super Resolution
"... Abstract—In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance ofthereconstructedHRimage.Inthispaper ..."
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Cited by 10 (2 self)
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Abstract—In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance ofthereconstructedHRimage.Inthispaper,we proposenovelsuper resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a Bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational Bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully Bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms. Index Terms—Bayesian methods, parameter estimation, super resolution, total variation, variational methods.
A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video
, 2007
"... Super-resolution (SR) reconstruction technique is capable of producing a high-resolution image from a sequence of low-resolution images. In this paper, we study an efficient SR algorithm for digital video. To effectively deal with the intractable problems in SR video reconstruction, such as inevitab ..."
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Cited by 10 (0 self)
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Super-resolution (SR) reconstruction technique is capable of producing a high-resolution image from a sequence of low-resolution images. In this paper, we study an efficient SR algorithm for digital video. To effectively deal with the intractable problems in SR video reconstruction, such as inevitable motion estimation errors, noise, blurring, missing regions, and compression artifacts, the total variation (TV) regularization is employed in the reconstruction model. We use the fixed-point iteration method and preconditioning techniques to efficiently solve the associated nonlinear Euler-Lagrange equations of the corresponding variational problem in SR. The proposed algorithm has been tested in several cases of motion and degradation. It is also compared with the Laplacian regularization-based SR algorithm and other TV-based SR algorithms. Experimental results are presented to illustrate the effectiveness of the proposed algorithm.
Maximum a Posteriori Video Super-Resolution Using a New Multichannel Image Prior
, 2010
"... Super-resolution (SR) is the term used to define the process of estimating a high-resolution (HR) image or a set of HR images from a set of low-resolution (LR) observations. In this paper we propose a class of SR algorithms based on the maximum a posteriori (MAP) framework. These algorithms utilize ..."
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Cited by 4 (1 self)
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Super-resolution (SR) is the term used to define the process of estimating a high-resolution (HR) image or a set of HR images from a set of low-resolution (LR) observations. In this paper we propose a class of SR algorithms based on the maximum a posteriori (MAP) framework. These algorithms utilize a new multichannel image prior model, along with the state-of-the-art single channel image prior and observation models. A hierarchical (twolevel) Gaussian nonstationary version of the multichannel prior is also defined and utilized within the same framework. Numerical experiments comparing the proposed algorithms among themselves and with other algorithms in the literature, demonstrate the advantages of the adopted multichannel approach.
Motion Segmentation: a Review
"... Abstract. Motion segmentation is an essential process for many computer vision algorithms. During the last decade, a large amount of work has been trying to tackle this challenge, however, performances of most of them still fall far behind human perception. In this paper the motion segmentation prob ..."
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Cited by 4 (1 self)
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Abstract. Motion segmentation is an essential process for many computer vision algorithms. During the last decade, a large amount of work has been trying to tackle this challenge, however, performances of most of them still fall far behind human perception. In this paper the motion segmentation problem is studied, analyzing and reviewing the most important and newest techniques. We propose a classification of all these techniques into different categories according to their main principle and features. Moreover, we point out their strengths and weaknesses and finally we suggest further research directions.
A Novel Method for Brain MRI Super-resolution by Wavelet-based POCS and Adaptive Edge Zoom
"... Abstract – This paper aims to make the super-resolution of a high-resolution image from a sequence of low-resolution frames containing non-stationary objects. The challenges of making super-resolution image, like unavoidable smoothing effects, introduction of artifacts, computational efficiency in t ..."
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Abstract – This paper aims to make the super-resolution of a high-resolution image from a sequence of low-resolution frames containing non-stationary objects. The challenges of making super-resolution image, like unavoidable smoothing effects, introduction of artifacts, computational efficiency in time and computational efficiency in memory requirements, are considered and a novel method is proposed to solve these problems. The proposed method handles the super-resolution process by using wavelet based projection-onto-convex-set with adaptive edge zoom algorithm. Adaptive edge zoom algorithm address the problem of producing enlarged picture from the given digital image. Wavelet based projection-onto-convex-set method is used to enhance spatial resolution of MRI brain images from a temporal sequence. This method produces more clarity with high peak signal-to-noise ratio.
