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53
Kernel regression for image processing and reconstruction
- IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2007
"... In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, ..."
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Cited by 172 (53 self)
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In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples.
Soft edge smoothness prior for alpha channel super resolution
- in CVPR
, 2007
"... Effective image prior is necessary for image super resolution, due to its severely under-determined nature. Although the edge smoothness prior can be effective, it is generally difficult to have analytical forms to evaluate the edge smoothness, especially for soft edges that exhibit gradual intensit ..."
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Cited by 36 (6 self)
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Effective image prior is necessary for image super resolution, due to its severely under-determined nature. Although the edge smoothness prior can be effective, it is generally difficult to have analytical forms to evaluate the edge smoothness, especially for soft edges that exhibit gradual intensity transitions. This paper finds the connection between the soft edge smoothness and a soft cut metric on an image grid by generalizing the Geocuts method [5], and proves that the soft edge smoothness measure approximates the average length of all level lines in an intensity image. This new finding not only leads to an analytical characterization of the soft edge smoothness prior, but also gives an intuitive geometric explanation. Regularizing the super resolution problem by this new form of prior can simultaneously minimize the length of all level lines, and thus resulting in visually appealing results. In addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image super resolution, by normalizing edge segments with their alpha channel description, to achieve a unified treatment of edges with different contrast and scale. 1.
A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution
- In IEEE Transactions on Image Processing
, 2007
"... Abstract—Super resolution image reconstruction allows the re-covery of a high-resolution (HR) image from several low-resolu-tion images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-reso-lution problem in which the scenes contain multipl ..."
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Cited by 21 (1 self)
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Abstract—Super resolution image reconstruction allows the re-covery of a high-resolution (HR) image from several low-resolu-tion images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-reso-lution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a pos-teriori (MAP) framework, which judiciously combines motion esti-mation, segmentation, and super resolution together. A cyclic coor-dinate descent optimization procedure is used to solve the MAP for-mulation, in which the motion fields, segmentation fields, and HR images are found in an alternate manner given the two others, re-spectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated condi-tional mode optimization method to obtain the segmentation fields. The proposed algorithm has been tested using a synthetic image sequence, the “Mobile and Calendar ” sequence, and the original “Motorcycle and Car ” sequence. The experiment results and error analyses verify the efficacy of this algorithm. Index Terms—Joint estimation, maximum a posteriori (MAP), motion estimation, segmentation, super resolution. I.
Color reproduction from noisy CFA data of single sensor digital cameras
- IEEE TRANS. IMAGE PROCESSING
, 2007
"... Single sensor digital color still/video cameras capture images using a color filter array (CFA) and require color interpolation (demosaicking) to reconstruct full color images. The color reproduction has to combat sensor noises which are channel dependent. If untreated in demosaicking, sensor noise ..."
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Cited by 18 (2 self)
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Single sensor digital color still/video cameras capture images using a color filter array (CFA) and require color interpolation (demosaicking) to reconstruct full color images. The color reproduction has to combat sensor noises which are channel dependent. If untreated in demosaicking, sensor noises can cause color artifacts that are hard to remove later by a separate denoising process, because the demosaicking process complicates the noise characteristics by blending noises of different color channels. This paper presents a joint demosaicking-denoising approach to overcome this difficulty. The color image is restored from noisy mosaic data in two steps. First, the difference signals of color channels are estimated by linear minimum mean square-error estimation. This process exploits both spectral and spatial correlations to simultaneously suppress sensor noise and interpolation error. With the estimated difference signals, the full resolution green channel is recovered. The second step involves in a wavelet-based denoising process to remove the CFA channel-dependent noises from the reconstructed green channel. The red and blue channels are subsequently recovered. Simulated and real CFA mosaic data are used to evaluate the performance of the proposed joint demosaicking-denoising scheme and compare it with many recently developed sophisticated demosaicking and denoising schemes.
SoftCuts: A Soft Edge Smoothness Prior for Color Image Super-Resolution
, 2009
"... Designing effective image priors is of great interest to image super-resolution (SR), which is a severely under-determined problem. An edge smoothness prior is favored since it is able to suppress the jagged edge artifact effectively. However, for soft image edges with gradual intensity transitions ..."
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Cited by 18 (0 self)
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Designing effective image priors is of great interest to image super-resolution (SR), which is a severely under-determined problem. An edge smoothness prior is favored since it is able to suppress the jagged edge artifact effectively. However, for soft image edges with gradual intensity transitions, it is generally difficult to obtain analytical forms for evaluating their smoothness. This paper characterizes soft edge smoothness based on a novel SoftCuts metric by generalizing the Geocuts method [1]. The proposed soft edge smoothness measure can approximate the average length of all level lines in an intensity image. Thus, the total length of all level lines can be minimized effectively by integrating this new form of prior. In addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image SR, by adaptively normalizing image edges according to their-channel description. This leads to the adaptive SoftCuts algorithm, which represents a unified treatment of edges with different contrasts and scales. Experimental results are presented which demonstrate the effectiveness of the proposed method.
SUPER RESOLUTION BASED ON FAST REGISTRATION AND MAXIMUM A POSTERIORI RECONSTRUCTION
"... In this paper we propose a maximum a posteriori (MAP) framework for the super resolution problem, i.e. reconstructing high-resolution images from shifted, rotated, low-resolution degraded observations. The main contributions of this work are two; first, the use of a new locally adaptive edge preserv ..."
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Cited by 15 (4 self)
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In this paper we propose a maximum a posteriori (MAP) framework for the super resolution problem, i.e. reconstructing high-resolution images from shifted, rotated, low-resolution degraded observations. The main contributions of this work are two; first, the use of a new locally adaptive edge preserving prior for the super resolution problem. Second an efficient two-step reconstruction methodology that includes first an initial registration using only the low resolution degraded observations. This is followed by a fast iterative algorithm implemented in the discrete Fourier transform domain in which the restoration, interpolation and the registration subtasks of this problem are preformed simultaneously. We present examples with both synthetic and real data that demonstrate the advantages of the proposed framework.
Video-to-Video Dynamic Super-Resolution for Grayscale and Color Sequences
, 2006
"... We address the dynamic super-resolution (SR) problem of reconstructing a high-quality set of monochromatic or color superresolved images from low-quality monochromatic, color, or mosaiced frames. Our approach includes a joint method for simultaneous SR, deblurring, and demosaicing, this way taking i ..."
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Cited by 13 (3 self)
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We address the dynamic super-resolution (SR) problem of reconstructing a high-quality set of monochromatic or color superresolved images from low-quality monochromatic, color, or mosaiced frames. Our approach includes a joint method for simultaneous SR, deblurring, and demosaicing, this way taking into account practical color measurements encountered in video sequences. For the case of translational motion and common space-invariant blur, the proposed method is based on a very fast and memory efficient approximation of the Kalman filter (KF). Experimental results on both simulated and real data are supplied, demonstrating the presented algorithms, and their strength.
A practical approach to super-resolution
- In Proc. of the SPIE: Visual Communications and Image Processing
, 2006
"... Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Super-Resolution (SR) methods are developed through the years to go beyond this limit by acquiring and fusing several low-resolution (LR) images of the same scene, producing a high-resolution (HR ..."
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Cited by 11 (2 self)
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Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Super-Resolution (SR) methods are developed through the years to go beyond this limit by acquiring and fusing several low-resolution (LR) images of the same scene, producing a high-resolution (HR) image. The early works on SR, although occasionally mathematically optimal for particular models of data and noise, produced poor results when applied to real images. In this paper, we discuss two of the main issues related to designing a practical SR system, namely reconstruction accuracy and computational efficiency. Reconstruction accuracy refers to the problem of designing a robust SR method applicable to images from different imaging systems. We study a general framework for optimal reconstruction of images from grayscale, color, or color filtered (CFA) cameras. The performance of our proposed method is boosted by using powerful priors and is robust to both measurement (e.g. CCD read out noise) and system noise (e.g. motion estimation error). Noting that the motion estimation is often considered a bottleneck in terms of SR performance, we introduce the concept of “constrained motions” for enhancing the quality of super-resolved images. We show that using such constraints will enhance the quality of the motion estimation and therefore results in more accurate reconstruction of the HR images. We also justify some practical assumptions that greatly reduce the computational complexity and memory requirements of the proposed methods. We use efficient approximation of the Kalman Filter (KF) and adopt a dynamic point of view to the SR problem. Novel methods for addressing these issues are accompanied by experimental results on real data. 1.
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.
Non-stationary correction of optical aberrations
- In Proceedings of the 13th International Conference on Computer Vision
, 2011
"... Figure 1. Self-made photographic lens with one glass element only. Taken image without and with lens correction. Taking a sharp photo at several megapixel resolution traditionally relies on high grade lenses. In this paper, we present an approach to alleviate image degradations caused by imperfect o ..."
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Cited by 8 (1 self)
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Figure 1. Self-made photographic lens with one glass element only. Taken image without and with lens correction. Taking a sharp photo at several megapixel resolution traditionally relies on high grade lenses. In this paper, we present an approach to alleviate image degradations caused by imperfect optics. We rely on a calibration step to encode the optical aberrations in a space-variant point spread function and obtain a corrected image by non-stationary deconvolution. By including the Bayer array in our image formation model, we can perform demosaicing as part of the deconvolution. 1.
