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109
TwoPhase Kernel Estimation for Robust Motion Deblurring
"... Abstract. We discuss a few new motion deblurring problems that are significant to kernel estimation and nonblind deconvolution. We found that strong edges do not always profit kernel estimation, but instead under certain circumstance degrade it. This finding leads to a new metric to measure the use ..."
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Cited by 92 (4 self)
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Abstract. We discuss a few new motion deblurring problems that are significant to kernel estimation and nonblind deconvolution. We found that strong edges do not always profit kernel estimation, but instead under certain circumstance degrade it. This finding leads to a new metric to measure the usefulness of image edges in motion deblurring and a gradient selection process to mitigate their possible adverse effect. We also propose an efficient and highquality kernel estimation method based on using the spatial prior and the iterative support detection (ISD) kernel refinement, which avoids hard threshold of the kernel elements to enforce sparsity. We employ the TVℓ1 deconvolution model, solved with a new variable substitution scheme to robustly suppress noise. 1
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 75 (4 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.
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 37 (3 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.
SpaceVariant SingleImage Blind Deconvolution for Removing Camera Shake
"... Modelling camera shake as a spaceinvariant convolution simplifies the problem of removing camera shake, but often insufficiently models actual motion blur such as those due to camera rotation and movements outside the sensor plane or when objects in the scene have different distances to the camera. ..."
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Cited by 23 (4 self)
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Modelling camera shake as a spaceinvariant convolution simplifies the problem of removing camera shake, but often insufficiently models actual motion blur such as those due to camera rotation and movements outside the sensor plane or when objects in the scene have different distances to the camera. In an effort to address these limitations, (i) we introduce a taxonomy of camera shakes, (ii) we build on a recently introduced framework for spacevariant filtering by Hirsch et al. and a fast algorithm for single image blind deconvolution for spaceinvariant filters by Cho and Lee to construct a method for blind deconvolution in the case of spacevariant blur, and (iii), we present an experimental setup for evaluation that allows us to take images with real camera shake while at the same time recording the spacevariant point spread function corresponding to that blur. Finally, we demonstrate that our method is able to deblur images degraded by spatiallyvarying blur originating from real camera shake, even without using additionally motion sensor information. 1
Close the Loop: Joint Blind Image Restoration and Recognition with Sparse Representation Prior
"... Most previous visual recognition systems simply assume ideal inputs without realworld degradations, such as low resolution, motion blur and outoffocus blur. In presence of such unknown degradations, the conventional approach first resorts to blind image restoration and then feeds the restored ima ..."
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Cited by 15 (2 self)
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Most previous visual recognition systems simply assume ideal inputs without realworld degradations, such as low resolution, motion blur and outoffocus blur. In presence of such unknown degradations, the conventional approach first resorts to blind image restoration and then feeds the restored image into a classifier. Treating restoration and recognition separately, such a straightforward approach, however, suffers greatly from the defective output of the illposed blind image restoration. In this paper, we present a joint blind image restoration and recognition method based on the sparse representation prior to handle the challenging problem of face recognition from lowquality images, where the degradation model is realistic and totally unknown. The sparse representation prior states that the degraded input image, if correctly restored, will have a good sparse representation in terms of the training set, which indicates the identity of the test image. The proposed algorithm achieves simultaneous restoration and recognition by iteratively solving the blind image restoration in pursuit of the sparest representation for recognition. Based on such a sparse representation prior, we demonstrate that the image restoration task and the recognition task can benefit greatly from each other. Extensive experiments on face datasets under various degradations are carried out and the results of our joint model shows significant improvements over conventional methods of treating the two tasks independently. 1.
Edgebased blur kernel estimation using patch priors
 In: ICCP. (2013
"... Blind image deconvolution, i.e., estimating a blur kernel k and a latent image x from an input blurred image y, is a severely illposed problem. In this paper we introduce a new patchbased strategy for kernel estimation in blind deconvolution. Our approach estimates a “trusted ” subset of x by imp ..."
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Cited by 14 (2 self)
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Blind image deconvolution, i.e., estimating a blur kernel k and a latent image x from an input blurred image y, is a severely illposed problem. In this paper we introduce a new patchbased strategy for kernel estimation in blind deconvolution. Our approach estimates a “trusted ” subset of x by imposing a patch prior specifically tailored towards modeling the appearance of image edge and corner primitives. To choose proper patch priors we examine both statistical priors learned from a natural image dataset and a simple patch prior from synthetic structures. We show that our patch prior prefers sharp image content to blurry ones. Based on the patch priors, we iteratively recover the partial latent image x and the blur kernel k. A comprehensive evaluation shows that our approach achieves stateoftheart results for uniformly blurred images. 1.
Deblurring Shaken and Partially Saturated Images
 INT J COMPUT VIS
, 2013
"... We address the problem of deblurring images degraded by camera shake blur and saturated (overexposed) pixels. Saturated pixels violate the common assumption that the imageformation process is linear, and often cause ringing in deblurred outputs. We provide an analysis of ringing in general, and ..."
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Cited by 13 (1 self)
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We address the problem of deblurring images degraded by camera shake blur and saturated (overexposed) pixels. Saturated pixels violate the common assumption that the imageformation process is linear, and often cause ringing in deblurred outputs. We provide an analysis of ringing in general, and show that in order to prevent ringing, it is insufficient to simply discard saturated pixels. We show that even when saturated pixels are removed, ringing is caused by attempting to estimate the values of latent pixels that are brighter than the sensor’s maximum output. Estimating these latent pixels is likely to cause large errors, and these errors propagate across the rest of the image in the form of ringing. We propose a new deblurring algorithm that locates these errorprone bright pixels in the latent sharp image, and by decoupling them from the remainder of the latent image, greatly reduces ringing. In addition, we propose an approximate forward model for saturated images, which allows us
Blurkernel estimation from spectral irregularities
 IN: COMPUTER VISION–ECCV 2012
, 2012
"... We describe a new method for recovering the blur kernel in motionblurred images based on statistical irregularities their power spectrum exhibits. This is achieved by a powerlaw that refines the one traditionally used for describing natural images. The new model better accounts for biases arisin ..."
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Cited by 13 (0 self)
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We describe a new method for recovering the blur kernel in motionblurred images based on statistical irregularities their power spectrum exhibits. This is achieved by a powerlaw that refines the one traditionally used for describing natural images. The new model better accounts for biases arising from the presence of large and strong edges in the image. We use this model together with an accurate spectral whiteing formula to estimate the power spectrum of the blur. The blur kernel is then recovered using a phase retrieval algorithm with improved convergence and disambiguation capabilities. Unlike many existing methods, the new approach does not perform a maximum a posteriori estimation, which involves repeated reconstructions of the latent image, and hence offers attractive running times. We compare the new method with stateoftheart methods and report various advantages, both in terms of efficiency and accuracy.
Bayesian deblurring with integrated noise estimation
 In IEEE Conf. Comput. Vision and Pattern Recognition
"... Conventional nonblind image deblurring algorithms involve natural image priors and maximum aposteriori (MAP) estimation. As a consequence of MAP estimation, separate preprocessing steps such as noise estimation and training of the regularization parameter are necessary to avoid user interaction. ..."
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Cited by 10 (5 self)
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Conventional nonblind image deblurring algorithms involve natural image priors and maximum aposteriori (MAP) estimation. As a consequence of MAP estimation, separate preprocessing steps such as noise estimation and training of the regularization parameter are necessary to avoid user interaction. Moreover, MAP estimates involving standard natural image priors have been found lacking in terms of restoration performance. To address these issues we introduce an integrated Bayesian framework that unifies nonblind deblurring and noise estimation, thus freeing the user of tediously predetermining a noise level. A samplingbased technique allows to integrate out the unknown noise level and to perform deblurring using the Bayesian minimum mean squared error estimate (MMSE), which requires no regularization parameter and yields higher performance than MAP estimates when combined with a learned highorder image prior. A quantitative evaluation demonstrates stateoftheart results for both nonblind deblurring and noise estimation. 1.