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15
Nonuniform deblurring for shaken images
 In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2010. 8. Image taken with a Canon 1D Mark III, at 35mm f/4.5. Images
"... Blur from camera shake is mostly due to the 3D rotation of the camera, resulting in a blur kernel that can be significantly nonuniform across the image. However, most current deblurring methods model the observed image as a convolution of a sharp image with a uniform blur kernel. We propose a new p ..."
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Cited by 37 (3 self)
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Blur from camera shake is mostly due to the 3D rotation of the camera, resulting in a blur kernel that can be significantly nonuniform across the image. However, most current deblurring methods model the observed image as a convolution of a sharp image with a uniform blur kernel. We propose a new parametrized geometric model of the blurring process in terms of the rotational velocity of the camera during exposure. We apply this model to two different algorithms for camera shake removal: the first one uses a single blurry image (blind deblurring), while the second one uses both a blurry image and a sharp but noisy image of the same scene. We show that our approach makes it possible to model and remove a wider class of blurs than previous approaches, including uniform blur as a special case, and demonstrate its effectiveness with experiments on real images. 1.
Single image deblurring using motion density functions
 In Proceedings of European Conference on Computer Vision
, 2010
"... Abstract. We present a novel single image deblurring method to estimate spatially nonuniform blur that results from camera shake. We use existing spatially invariant deconvolution methods in a local and robust way to compute initial estimates of the latent image. The camera motion is represented as ..."
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Cited by 20 (2 self)
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Abstract. We present a novel single image deblurring method to estimate spatially nonuniform blur that results from camera shake. We use existing spatially invariant deconvolution methods in a local and robust way to compute initial estimates of the latent image. The camera motion is represented as a Motion Density Function (MDF) which records the fraction of time spent in each discretized portion of the space of all possible camera poses. Spatially varying blur kernels are derived directly from the MDF. We show that 6D camera motion is well approximated by 3 degrees of motion (inplane translation and rotation) and analyze the scope of this approximation. We present results on both synthetic and captured data. Our system outperforms current approaches which make the assumption of spatially invariant blur. 1
B.: Fast removal of nonuniform camera shake
 In: ICCV
"... Camera shake leads to nonuniform image blurs. Stateoftheart methods for removing camera shake model the blur as a linear combination of homographically transformed versions of the true image. While this is conceptually interesting, the resulting algorithms are computationally demanding. In this p ..."
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Cited by 13 (2 self)
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Camera shake leads to nonuniform image blurs. Stateoftheart methods for removing camera shake model the blur as a linear combination of homographically transformed versions of the true image. While this is conceptually interesting, the resulting algorithms are computationally demanding. In this paper we develop a forward model based on the efficient filter flow framework, incorporating the particularities of camera shake, and show how an efficient algorithm for blur removal can be obtained. Comprehensive comparisons on a number of realworld blurry images show that our approach is not only substantially faster, but it also leads to better deblurring results. 1.
Motion Regularization for Matting Motion Blurred Objects
"... Abstract—This paper addresses the problem of matting motion blurred objects from a single image. Existing single image matting methods are designed to extract static objects that have fractional pixel occupancy. This arises because the physical scene object has a finer resolution than the discrete i ..."
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Cited by 4 (2 self)
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Abstract—This paper addresses the problem of matting motion blurred objects from a single image. Existing single image matting methods are designed to extract static objects that have fractional pixel occupancy. This arises because the physical scene object has a finer resolution than the discrete image pixel and therefore only occupies a fraction of the pixel. For a motion blurred object, however, fractional pixel occupancy is attributed to the object’s motion over the exposure period. While conventional matting techniques can be used to matte motion blurred objects, they are not formulated in a manner that considers the object’s motion and tend to work only when the object is on a homogeneous background. We show how to obtain better alpha mattes by introducing a regularization term in the matting formulation to account for the object’s motion. In addition, we outline a method for estimating local object motion based on local gradient statistics from the original image. For the sake of completeness, we also discuss how user markup can be used to denote the local direction in lieu of motion estimation. Improvements to alpha mattes computed with our regularization are demonstrated on a variety of examples. Index Terms—Matting, regularization, motion direction estimation, motion blur. 1
Nonlinear Camera Response Functions and . . .
, 2012
"... This paper investigates the role that nonlinear camera response functions (CRFs) have on image deblurring. In particular, we show how nonlinear CRFs can cause a spatially invariant blur to behave as a spatially varying blur. This can result in noticeable ringing artifacts when deconvolution is appli ..."
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Cited by 2 (1 self)
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This paper investigates the role that nonlinear camera response functions (CRFs) have on image deblurring. In particular, we show how nonlinear CRFs can cause a spatially invariant blur to behave as a spatially varying blur. This can result in noticeable ringing artifacts when deconvolution is applied even with a known point spread function (PSF). In addition, we show how CRFs can adversely affect PSF estimation algorithms in the case of blind deconvolution. To help counter these effects, we introduce two methods to estimate the CRF directly from one or more blurred images when the PSF is known or unknown. While not as accurate as conventional CRF estimation algorithms based on multiple exposures or calibration patterns, our approach is still quite effective in improving deblurring results in situations where the CRF is unknown.
Removing Motion Blur with SpaceTime Processing
, 2010
"... Although spatial deblurring is relatively wellunderstood by assuming that the blur kernel is shiftinvariant, motion blur is not so when we attempt to deconvolve this motion blur on a framebyframe basis: this is because, in general, videos include complex, multilayer transitions. Indeed, we face ..."
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Cited by 2 (0 self)
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Although spatial deblurring is relatively wellunderstood by assuming that the blur kernel is shiftinvariant, motion blur is not so when we attempt to deconvolve this motion blur on a framebyframe basis: this is because, in general, videos include complex, multilayer transitions. Indeed, we face an exceedingly difficult problem in motion deblurring of a single frame when the scene contains motion occlusions. Instead of deblurring video frames individually, a fully 3D deblurring method is proposed in this paper to reduce motion blur from a single motionblurred video to produce a high resolution video in both space and time. The blur kernel is free from explicit knowledge of local motions unlike other existing motionbased deblurring approaches. Most importantly, due to its inherent locally adaptive nature, the 3D deblurring is capable of automatically deblurring the portions of the sequence which are motion blurred, without segmentation, and without adversely affecting the rest of the spatiotemporal domain where such blur is not present. Our proposed approach is a twostep approach; first we upscale the input video in space and time without explicit estimates of local motions and then perform 3D deblurring to obtain the restored sequence.
Registration Based Nonuniform Motion Deblurring
"... Figure 1: Nonuniform motion deblurring: (a)&(b) Input images and nonuniform blur kernels estimated by our method, (c) Our nonuniform motion deblurring result, (d) Uniform motion deblurring result [CL09]. This paper proposes an algorithm which uses image registration to estimate a nonuniform ..."
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Figure 1: Nonuniform motion deblurring: (a)&(b) Input images and nonuniform blur kernels estimated by our method, (c) Our nonuniform motion deblurring result, (d) Uniform motion deblurring result [CL09]. This paper proposes an algorithm which uses image registration to estimate a nonuniform motion blur point spread function (PSF) caused by camera shake. Our study is based on a motion blur model which models blur effects of camera shakes using a set of planar perspective projections (i.e., homographies). This representation can fully describe motions of camera shakes in 3D which cause nonuniform motion blurs. We transform the nonuniform PSF estimation problem into a set of image registration problems which estimate homographies of the motion blur model onebyone through the LucasKanade algorithm. We demonstrate the performance of our algorithm using both synthetic and real world examples. We also discuss the effectiveness and limitations of our algorithm for nonuniform deblurring. Categories and Subject Descriptors (according to ACM CCS): Enhancement—Sharpening and deblurring I.4.3 [Image Processing and Computer Vision]: 1.
Motion Deblurring from a Single Image using Circular Sensor Motion
, 2011
"... Image blur caused by object motion attenuates high frequency content of images, making postcapture deblurring an illposed problem. The recoverable frequency band quickly becomes narrower for faster object motion as high frequencies are severely attenuated and virtually lost. This paper proposes to ..."
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Image blur caused by object motion attenuates high frequency content of images, making postcapture deblurring an illposed problem. The recoverable frequency band quickly becomes narrower for faster object motion as high frequencies are severely attenuated and virtually lost. This paper proposes to translate a camera sensor circularly about the optical axis during exposure, so that high frequencies can be preserved for a wide range of inplane linear object motion in any direction within some predetermined speed. That is, although no object may be photographed sharply at capture time, differently moving objects captured in a single image can be deconvolved with similar quality. In addition, circular sensor motion is shown to facilitate blur estimation thanks to distinct frequency zero patterns of the resulting motion blur pointspread functions. An analysis of the frequency characteristics of circular sensor motion in relation to linear object motion is presented, along with deconvolution results for photographs captured with a prototype camera.
Nonlinear Camera Response . . .
"... This paper investigates the role that nonlinear camera response functions (CRFs) have on image deblurring. In particular, we show how nonlinear CRFs can cause a spatially invariant blur to behave as a spatially varying blur. This can result in noticeable ringing artifacts when deconvolution is appli ..."
Abstract
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This paper investigates the role that nonlinear camera response functions (CRFs) have on image deblurring. In particular, we show how nonlinear CRFs can cause a spatially invariant blur to behave as a spatially varying blur. This can result in noticeable ringing artifacts when deconvolution is applied even with a known point spread function (PSF). In addition, we show how CRFs can adversely affect PSF estimation algorithms in the case of blind deconvolution. To help counter these effects, we introduce two methods to estimate the CRF directly from one or more blurred images when the PSF is known or unknown. While not as accurate as conventional CRF estimation algorithms based on multiple exposures or calibration patterns, our approach is still quite effective in improving deblurring results in situations where the CRF is unknown.
1 Motion Regularization for Matting Motion Blurred Objects
"... Abstract — This paper addresses the problem of matting motion blurred objects from a single image. Existing single image matting methods are designed to extract static objects that have fractional pixel occupancy. This arises because the physical scene object has a finer resolution than the discrete ..."
Abstract
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Abstract — This paper addresses the problem of matting motion blurred objects from a single image. Existing single image matting methods are designed to extract static objects that have fractional pixel occupancy. This arises because the physical scene object has a finer resolution than the discrete image pixel and therefore only occupies a fraction of the pixel. For a motion blurred object, however, fractional pixel occupancy is attributed to the object’s motion over the exposure period. While conventional matting techniques can be used to matte motion blurred objects, they are not formulated in a manner that considers the object’s motion and tend to work only when the object is on a homogeneous background. We show how to obtain better alpha mattes by introducing a regularization term in the matting formulation to account for the object’s motion. In addition, we outline a method for estimating local object motion based on local gradient statistics from the original image. For completeness sake, we also discuss how user markup can be used to denote the local direction in lieu of motion estimation. Improvements to alpha mattes computed with our regularization are demonstrated on a variety of examples. Index Terms — Matting, regularization, motion direction estimation, motion blur. I.