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49
Fast Motion Deblurring
"... This paper presents a fast deblurring method that produces a deblurring result from a single image of moderate size in a few seconds. We accelerate both latent image estimation and kernel estimation in an iterative deblurring process by introducing a novel prediction step and working with image deri ..."
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Cited by 128 (12 self)
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This paper presents a fast deblurring method that produces a deblurring result from a single image of moderate size in a few seconds. We accelerate both latent image estimation and kernel estimation in an iterative deblurring process by introducing a novel prediction step and working with image derivatives rather than pixel values. In the prediction step, we use simple image processing techniques to predict strong edges from an estimated latent image, which will be solely used for kernel estimation. With this approach, a computationally efficient Gaussian prior becomes sufficient for deconvolution to estimate the latent image, as small deconvolution artifacts can be suppressed in the prediction. For kernel estimation, we formulate the optimization function using image derivatives, and accelerate the numerical process by reducing the number of Fourier transforms needed for a conjugate gradient method. We also show that the formulation results in a smaller condition number of the numerical system than the use of pixel values, which gives faster convergence. Experimental results demonstrate that our method runs an order of magnitude faster than previous work, while the deblurring quality is comparable. GPU implementation facilitates further speedup, making our method fast enough for practical use.
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 50 (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
RichardsonLucy Deblurring for Scenes under Projective Motion Path
"... This paper addresses the problem of modeling and correcting image blur caused by camera motion that follows a projective motion path. We introduce a new Projective Motion Blur Model that treats the blurred image as an integration of a clear scene under a sequence of projective transformations that d ..."
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Cited by 35 (5 self)
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This paper addresses the problem of modeling and correcting image blur caused by camera motion that follows a projective motion path. We introduce a new Projective Motion Blur Model that treats the blurred image as an integration of a clear scene under a sequence of projective transformations that describe the camera’s path. The benefits of this motion blur model is that it compactly represents spatially varying motion blur without the need for explicit blurs kernels or having to segment the image into local regions with the same spatially invariant blur. We show how to modify the RichardsonLucy (RL) algorithm to incorporate our Projective Motion Blur Model to estimate the original clear image. In addition, we will show that our Projective Motion RL algorithm can incorporate stateoftheart regularization priors to improve the deblurred results. Our Projective Motion Blur Model along with the Projective Motion RL is detailed together with statistical analysis on the algorithm’s convergence properties, robustness to noise, and experimental results demonstrating its overall effectiveness for deblurring images.
Invertible motion blur in video
 ACM Trans. Graph
, 2009
"... Figure 1: By simply varying the exposure time for video frames, multiimage deblurring can be made invertible. (Left) Varying exposure photos of a moving car. Notice the change in illumination and the blur size in the captured photos. (Right) The foreground object is automatically rectified, segment ..."
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Cited by 29 (2 self)
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Figure 1: By simply varying the exposure time for video frames, multiimage deblurring can be made invertible. (Left) Varying exposure photos of a moving car. Notice the change in illumination and the blur size in the captured photos. (Right) The foreground object is automatically rectified, segmented, deblurred, and composed onto the background using the varying exposure video. Novel renderings, such as motion streaks, can be generated by linear combination of the deblurred image and the captured photos. We show that motion blur in successive video frames is invertible even if the pointspread function (PSF) due to motion smear in a single photo is noninvertible. Blurred photos exhibit nulls in the frequency transform of the PSF, leading to an illposed deconvolution. Hardware solutions to avoid this require specialized devices such as the coded exposure camera or accelerating sensor motion. We employ ordinary video cameras and introduce the notion of nullfilling along with jointinvertibility of multiple blurfunctions. The key idea is to record the same object with varying PSF’s, so that the nulls in the frequency component of one frame can be filled by other frames. The combined frequency transform becomes nullfree, making deblurring wellposed. We achieve jointlyinvertible blur simply by changing the exposure time of successive frames. We address the problem of automatic deblurring of objects moving with constant velocity by solving its critical components: preservation of all spatial frequencies, segmentation and motion estimation of moving parts, and nondegradation of the static parts of the scene. We demonstrate several challenging cases of object motion blur including textured backgrounds and partial occluders.
An augmented Lagrangian method for total variation video restoration,”
 IEEE Trans. Image Process.,
, 2011
"... AbstractThis paper presents a fast algorithm for restoring video sequences. The proposed algorithm, as opposed to existing methods, does not consider video restoration as a sequence of image restoration problems. Rather, it treats a video sequence as a spacetime volume and poses a spacetime tota ..."
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Cited by 25 (6 self)
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AbstractThis paper presents a fast algorithm for restoring video sequences. The proposed algorithm, as opposed to existing methods, does not consider video restoration as a sequence of image restoration problems. Rather, it treats a video sequence as a spacetime volume and poses a spacetime total variation regularization to enhance the smoothness of the solution. The optimization problem is solved by transforming the original unconstrained minimization problem to an equivalent constrained minimization problem. An augmented Lagrangian method is used to handle the constraints, and an alternating direction method (ADM) is used to iteratively find solutions of the subproblems. The proposed algorithm has a wide range of applications, including video deblurring and denoising, video disparity refinement, and hotair turbulence effect reduction.
Coded Exposure Deblurring: Optimized Codes for PSF Estimation and Invertibility
"... We consider the problem of single image object motion deblurring from a static camera. It is wellknown that deblurring of moving objects using a traditional camera is illposed, due to the loss of high spatial frequencies in the captured blurred image. A coded exposure camera [17] modulates the inte ..."
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Cited by 22 (2 self)
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We consider the problem of single image object motion deblurring from a static camera. It is wellknown that deblurring of moving objects using a traditional camera is illposed, due to the loss of high spatial frequencies in the captured blurred image. A coded exposure camera [17] modulates the integration pattern of light by opening and closing the shutter within the exposure time using a binary code. The code is chosen to make the resulting point spread function (PSF) invertible, for best deconvolution performance. However, for a successful deconvolution algorithm, PSF estimation is as important as PSF invertibility. We show that PSF estimation is easier if the resulting motion blur is smooth and the optimal code for PSF invertibility could worsen PSF estimation, since it leads to nonsmooth blur. We show that both criterions of PSF invertibility and PSF estimation can be simultaneously met, albeit with a slight increase in the deconvolution noise. We propose design rules for a code to have good PSF estimation capability and outline two search criteria for finding the optimal code for a given length. We present theoretical analysis comparing the performance of the proposed code with the code optimized solely for PSF invertibility. We also show how to easily implement coded exposure on a consumer grade machine vision camera with no additional hardware. Real experimental results demonstrate the effectiveness of the proposed codes for motion deblurring.
Generating Sharp Panoramas from Motionblurred Videos
"... In this paper, we show how to generate a sharp panorama from a set of motionblurred video frames. Our technique is based on joint global motion estimation and multiframe deblurring. It also automatically computes the duty cycle of the video, namely the percentage of time between frames that is act ..."
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Cited by 17 (1 self)
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In this paper, we show how to generate a sharp panorama from a set of motionblurred video frames. Our technique is based on joint global motion estimation and multiframe deblurring. It also automatically computes the duty cycle of the video, namely the percentage of time between frames that is actually exposure time. The duty cycle is necessary for allowing the blur kernels to be accurately extracted and then removed. We demonstrate our technique on a number of videos. 1.
Coded exposure imaging for projective motion deblurring
 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2010
"... We propose a method for deblurring of spatially variant object motion. A principal challenge of this problem is how to estimate the point spread function (PSF) of the spatially variant blur. Based on the projective motion blur model of [27], we present a blur estimation technique that jointly utiliz ..."
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Cited by 16 (5 self)
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We propose a method for deblurring of spatially variant object motion. A principal challenge of this problem is how to estimate the point spread function (PSF) of the spatially variant blur. Based on the projective motion blur model of [27], we present a blur estimation technique that jointly utilizes a coded exposure camera and simple user interactions to recover the PSF. With this spatially variant PSF, objects that exhibit projective motion can be effectively deblurred. We validate this method with several challenging image examples. (a) (b)
Optimal Single Image Capture for Motion Deblurring
"... Deblurring images of moving objects captured from a traditional camera is an illposed problem due to the loss of high spatial frequencies in the captured images. Recent techniques have attempted to engineer the motion point spread function (PSF) by either making it invertible [16] using coded expos ..."
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Cited by 15 (1 self)
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Deblurring images of moving objects captured from a traditional camera is an illposed problem due to the loss of high spatial frequencies in the captured images. Recent techniques have attempted to engineer the motion point spread function (PSF) by either making it invertible [16] using coded exposure, or invariant to motion [13] by moving the camera in a specific fashion. We address the problem of optimal single image capture strategy for best deblurring performance. We formulate the problem of optimal capture as maximizing the signal to noise ratio (SNR) of the deconvolved image given a scene light level. As the exposure time increases, the sensor integrates more light, thereby increasing the SNR of the captured signal. However, for moving objects, larger exposure time also results in more blur and hence more deconvolution noise. We compare the following three single image capture strategies: (a) traditional camera, (b) coded exposure camera, and (c) motion invariant photography, as well as the best exposure time for capture by analyzing the rate of increase of deconvolution noise with exposure time. We analyze which strategy is optimal for known/unknown motion direction and speed and investigate how the performance degrades for other cases. We present real experimental results by simulating the above capture strategies using a high speed video camera. 1.
Analyzing Spatiallyvarying Blur
"... c○2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional ..."
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Cited by 15 (0 self)
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c○2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional