Results 1  10
of
274
Highquality Motion Deblurring from a Single Image
, 2008
"... Figure 1 High quality single image motiondeblurring. The left subfigure shows one captured image using a handheld camera under dim light. It is severely blurred by an unknown kernel. The right subfigure shows our deblurred image result computed by estimating both the blur kernel and the unblurre ..."
Abstract

Cited by 174 (6 self)
 Add to MetaCart
Figure 1 High quality single image motiondeblurring. The left subfigure shows one captured image using a handheld camera under dim light. It is severely blurred by an unknown kernel. The right subfigure shows our deblurred image result computed by estimating both the blur kernel and the unblurred latent image. We show several closeups of blurred/unblurred image regions for comparison. We present a new algorithm for removing motion blur from a single image. Our method computes a deblurred image using a unified probabilistic model of both blur kernel estimation and unblurred image restoration. We present an analysis of the causes of common artifacts found in current deblurring methods, and then introduce several novel terms within this probabilistic model that are inspired by our analysis. These terms include a model of the spatial randomness of noise in the blurred image, as well a new local smoothness prior that reduces ringing artifacts by constraining contrast in the unblurred image wherever the blurred image exhibits low contrast. Finally, we describe an efficient optimization scheme that alternates between blur kernel estimation and unblurred image restoration until convergence. As a result of these steps, we are able to produce high quality deblurred results in low computation time. We are even able to produce results of comparable quality to techniques that require additional input images beyond a single blurry photograph, and to methods that require additional hardware.
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 ..."
Abstract

Cited by 124 (12 self)
 Add to MetaCart
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.
EdgePreserving Decompositions for MultiScale . . .
"... Many recent computational photography techniques decompose an image into a piecewise smooth base layer, containing large scale variations in intensity, and a residual detail layer capturing the smaller scale details in the image. In many of these applications, it is important to control the spatial ..."
Abstract

Cited by 111 (6 self)
 Add to MetaCart
Many recent computational photography techniques decompose an image into a piecewise smooth base layer, containing large scale variations in intensity, and a residual detail layer capturing the smaller scale details in the image. In many of these applications, it is important to control the spatial scale of the extracted details, and it is often desirable to manipulate details at multiple scales, while avoiding visual artifacts. In this paper we introduce a new way to construct edgepreserving multiscale image decompositions. We show that current basedetail decomposition techniques, based on the bilateral filter, are limited in their ability to extract detail at arbitrary scales. Instead, we advocate the use of an alternative edgepreserving smoothing operator, based on the weighted least squares optimization framework, which is particularly well suited for progressive coarsening of images and for multiscale detail extraction. After describing this operator, we show how to use it to construct edgepreserving multiscale decompositions, and compare it to the bilateral filter, as well as to other schemes. Finally, we demonstrate the effectiveness of our edgepreserving decompositions in the context of LDR and HDR tone mapping, detail enhancement, and other applications.
Fast image deconvolution using hyperlaplacian priors, supplementary material
, 2009
"... The heavytailed distribution of gradients in natural scenes have proven effective priors for a range of problems such as denoising, deblurring and superresolution. These distributions are well modeled by a hyperLaplacian p(x) ∝ e−kxα), typically with 0.5 ≤ α ≤ 0.8. However, the use of sparse ..."
Abstract

Cited by 106 (1 self)
 Add to MetaCart
(Show Context)
The heavytailed distribution of gradients in natural scenes have proven effective priors for a range of problems such as denoising, deblurring and superresolution. These distributions are well modeled by a hyperLaplacian p(x) ∝ e−kxα), typically with 0.5 ≤ α ≤ 0.8. However, the use of sparse distributions makes the problem nonconvex and impractically slow to solve for multimegapixel images. In this paper we describe a deconvolution approach that is several orders of magnitude faster than existing techniques that use hyperLaplacian priors. We adopt an alternating minimization scheme where one of the two phases is a nonconvex problem that is separable over pixels. This perpixel subproblem may be solved with a lookup table (LUT). Alternatively, for two specific values of α, 1/2 and 2/3 an analytic solution can be found, by finding the roots of a cubic and quartic polynomial, respectively. Our approach (using either LUTs or analytic formulae) is able to deconvolve a 1 megapixel image in less than ∼3 seconds, achieving comparable quality to existing methods such as iteratively reweighted least squares (IRLS) that take ∼20 minutes. Furthermore, our method is quite general and can easily be extended to related image processing problems, beyond the deconvolution application demonstrated. 1
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 ..."
Abstract

Cited by 93 (4 self)
 Add to MetaCart
(Show Context)
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
Single image dehazing
 In Proc. ACM SIGGRAPH
"... Figure 1: Dehazing based on a single input image and the corresponding depth estimate. In this paper we present a new method for estimating the optical transmission in hazy scenes given a single input image. Based on this estimation, the scattered light is eliminated to increase scene visibility and ..."
Abstract

Cited by 87 (2 self)
 Add to MetaCart
Figure 1: Dehazing based on a single input image and the corresponding depth estimate. In this paper we present a new method for estimating the optical transmission in hazy scenes given a single input image. Based on this estimation, the scattered light is eliminated to increase scene visibility and recover hazefree scene contrasts. In this new approach we formulate a refined image formation model that accounts for surface shading in addition to the transmission function. This allows us to resolve ambiguities in the data by searching for a solution in which the resulting shading and transmission functions are locally statistically uncorrelated. A similar principle is used to estimate the color of the haze. Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.
H.: Progressive interscale and intrascale nonblind image deconvolution
 In ACM SIGGRAPH
, 2008
"... et al. 2006]), standard RichardsonLucy (RL) [Lucy 1974] result, and our result. Abstract. Ringing is the most disturbing artifact in the image deconvolution. In this paper, we present a progressive interscale and intrascale nonblind image deconvolution approach that significantly reduces ringing ..."
Abstract

Cited by 56 (2 self)
 Add to MetaCart
et al. 2006]), standard RichardsonLucy (RL) [Lucy 1974] result, and our result. Abstract. Ringing is the most disturbing artifact in the image deconvolution. In this paper, we present a progressive interscale and intrascale nonblind image deconvolution approach that significantly reduces ringing. Our approach is built on a novel edgepreserving deconvolution algorithm called bilateral RichardsonLucy (BRL) which uses a large spatial support to handle large blur. We progressively recover the image from a coarse scale to a fine scale (interscale), and progressively restore image details within every scale (intrascale). To perform the interscale deconvolution, we propose a joint bilateral RichardsonLucy (JBRL) algorithm so that the recovered image in one scale can guide the deconvolution in the next scale. In each scale, we propose an iterative residual deconvolution to progressively recover image details. The experimental results show that our progressive deconvolution can produce images with very little ringing for large blur kernels. 1
Motion from blur
 In Proc. Conf. Computer Vision and Pattern Recognition
, 2008
"... Motion blur retains some information about motion, based on which motion may be recovered from blurred images. This is a difficult problem, as the situations of motion blur can be quite complicated, such as they may be spacevariant, nonlinear, and local. This paper addresses a very challenging probl ..."
Abstract

Cited by 47 (2 self)
 Add to MetaCart
(Show Context)
Motion blur retains some information about motion, based on which motion may be recovered from blurred images. This is a difficult problem, as the situations of motion blur can be quite complicated, such as they may be spacevariant, nonlinear, and local. This paper addresses a very challenging problem: can we recover motion blindly from a single motionblurred image? A major contribution of this paper is a new finding of an elegant motion blur constraint. Exhibiting a very similar mathematical form as the optical flow constraint, this linear constraint applies locally to pixels in the image. Therefore, a number of challenging problems can be unified, including estimating global affine motion blur, estimating global rotational motion blur, estimating and segmenting multiple motion blur, and estimating nonparametric motion blur field. Extensive experiments on blur estimation and image deblurring on both synthesized and real data demonstrate the accuracy and general applicability of the proposed approach. 1.
Robust dual motion deblurring
 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2008
"... This paper presents a robust algorithm to deblur two consecutively captured blurred photos from camera shaking. Previous dual motion deblurring algorithms succeeded in small and simple motion blur and are very sensitive to noise. We develop a robust feedback algorithm to perform iteratively kernel e ..."
Abstract

Cited by 46 (0 self)
 Add to MetaCart
(Show Context)
This paper presents a robust algorithm to deblur two consecutively captured blurred photos from camera shaking. Previous dual motion deblurring algorithms succeeded in small and simple motion blur and are very sensitive to noise. We develop a robust feedback algorithm to perform iteratively kernel estimation and image deblurring. In kernel estimation, the stability and capability of the algorithm is greatly improved by incorporating a robust cost function and a set of kernel priors. The robust cost function serves to reject outliers and noise, while kernel priors, including sparseness and continuity, remove ambiguity and maintain kernel shape. In deblurring, we propose a novel and robust approach which takes two blurred images as input to infer the clear image. The deblurred image is then used as feedback to refine kernel estimation. Our method can successfully estimate large and complex motion blurs which cannot be handled by previous dual or single image motion deblurring algorithms. The results are shown to be significantly better than those of previous approaches. 1.