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110
Guided Image Filtering
"... Abstract. In this paper, we propose a novel type of explicit image filter- guided filter. Derived from a local linear model, the guided filter generates the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided fil ..."
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Cited by 145 (1 self)
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Abstract. In this paper, we propose a novel type of explicit image filter- guided filter. Derived from a local linear model, the guided filter generates the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can perform as an edge-preserving smoothing operator like the popular bilateral filter [1], but has better behavior near the edges. It also has a theoretical connection with the matting Laplacian matrix [2], so is a more generic concept than a smoothing operator and can better utilize the structures in the guidance image. Moreover, the guidedfilterhasafastandnon-approximatelinear-time algorithm, whose computational complexity is independent of the filtering kernel size. We demonstrate that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling. 1
Single image haze removal using dark channel prior
- In CVPR
, 2009
"... Abstract In this paper, we propose a simple but effective image prior- dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation- most local patches in haze-free outdoor images contain ..."
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Cited by 130 (4 self)
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Abstract In this paper, we propose a simple but effective image prior- dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation- most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high quality depth map can also be obtained as a by-product of haze removal. 1.
Photo clip art
- ACM Transactions on Graphics (SIGGRAPH
, 2007
"... Figure 1: Starting with a present day photograph of the famous Abbey Road in London (left), a person using our system was easily able to make the scene much more lively. There are 4 extra objects in the middle image, and 17 extra in the right image. Can you spot them all? We present a system for ins ..."
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Cited by 94 (20 self)
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Figure 1: Starting with a present day photograph of the famous Abbey Road in London (left), a person using our system was easily able to make the scene much more lively. There are 4 extra objects in the middle image, and 17 extra in the right image. Can you spot them all? We present a system for inserting new objects into existing photographs by querying a vast image-based object library, precomputed using a publicly available Internet object database. The central goal is to shield the user from all of the arduous tasks typically involved in image compositing. The user is only asked to do two simple things: 1) pick a 3D location in the scene to place a new object; 2) select an object to insert using a hierarchical menu. We pose the problem of object insertion as a data-driven, 3D-based, context-sensitive object retrieval task. Instead of trying to manipulate the object to change its orientation, color distribution, etc. to fit the new image, we simply retrieve an object of a specified class that has all the required properties (camera pose, lighting, resolution, etc) from our large object library. We present new automatic algorithms for improving object segmentation and blending, estimating true 3D object size and orientation, and estimating scene lighting conditions. We also present an intuitive user interface that makes object insertion fast and simple even for the artistically challenged.
Optimized color sampling for robust matting
- In In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
, 2007
"... Image matting is the problem of determining for each pixel in an image whether it is foreground, background, or the mixing parameter, ”alpha”, for those pixels that are a mixture of foreground and background. Matting is inher-ently an ill-posed problem. Previous matting approaches either use naive c ..."
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Cited by 75 (5 self)
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Image matting is the problem of determining for each pixel in an image whether it is foreground, background, or the mixing parameter, ”alpha”, for those pixels that are a mixture of foreground and background. Matting is inher-ently an ill-posed problem. Previous matting approaches either use naive color sampling methods to estimate fore-ground and background colors for unknown pixels, or use propagation-based methods to avoid color sampling under weak assumptions about image statistics. We argue that nei-ther method itself is enough to generate good results for complex natural images. We analyze the weaknesses of previous matting ap-proaches, and propose a new robust matting algorithm. In our approach we also sample foreground and background colors for unknown pixels, but more importantly, analyze the confidence of these samples. Only high confidence sam-ples are chosen to contribute to the matting energy function which is minimized by a Random Walk. The energy func-tion we define also contains a neighborhood term to en-force the smoothness of the matte. To validate the approach, we present an extensive and quantitative comparison be-tween our algorithm and a number of previous approaches in hopes of providing a benchmark for future matting re-search. 1.
Spectral Matting
, 2008
"... We present spectral matting: a new approach to natural image matting that automatically computes a set of fundamental fuzzy matting components from the smallest eigenvectors of a suitably defined Laplacian matrix. Thus, our approach extends spectral segmentation techniques, whose goal is to extract ..."
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Cited by 60 (2 self)
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We present spectral matting: a new approach to natural image matting that automatically computes a set of fundamental fuzzy matting components from the smallest eigenvectors of a suitably defined Laplacian matrix. Thus, our approach extends spectral segmentation techniques, whose goal is to extract hard segments, to the extraction of soft matting components. These components may then be used as building blocks to easily construct semantically meaningful foreground mattes, either in an unsupervised fashion, or based on a small amount of user input. 1.
Image/video deblurring using a hybrid camera
- In IEEE CVPR
, 2008
"... We propose a novel approach to reduce spatially varying motion blur using a hybrid camera system that simultaneously captures high-resolution video at a low-frame rate together with low-resolution video at a high-frame rate. Our work is inspired by Ben-Ezra and Nayar [3] who introduced the hybrid ca ..."
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Cited by 50 (6 self)
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We propose a novel approach to reduce spatially varying motion blur using a hybrid camera system that simultaneously captures high-resolution video at a low-frame rate together with low-resolution video at a high-frame rate. Our work is inspired by Ben-Ezra and Nayar [3] who introduced the hybrid camera idea for correcting global motion blur for a single still image. We broaden the scope of the problem to address spatially varying blur as well as video imagery. We also reformulate the correction process to use more information available in the hybrid camera system, as well as iteratively refine spatially varying motion extracted from the low-resolution high-speed camera. We demonstrate that our approach achieves superior results over existing work and can be extended to deblurring of moving objects. 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 ..."
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Cited by 49 (2 self)
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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 motion-blurred 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.
Rotational motion deblurring of a rigid object from a single image
- In ICCV
, 2007
"... Most previous motion deblurring methods restore the degraded image assuming a shift-invariant linear blur filter. These methods are not applicable if the blur is caused by spatially variant motions. In this paper, we model the physical properties of a 2-D rigid body movement and propose a practical ..."
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Cited by 46 (5 self)
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Most previous motion deblurring methods restore the degraded image assuming a shift-invariant linear blur filter. These methods are not applicable if the blur is caused by spatially variant motions. In this paper, we model the physical properties of a 2-D rigid body movement and propose a practical framework to deblur rotational motions from a single image. Our main observation is that the transparency cue of a blurred object, which represents the motion blur formation from an imaging perspective, provides sufficient information in determining the object movements. Comparatively, single image motion deblurring using pixel color/gradient information has large uncertainties in motion representation and computation. Our results are produced by minimizing a new energy function combining rotation, possible translations, and the transparency map using an iterative optimizing process. The effectiveness of our method is demonstrated using challenging image examples. anteed since the convolution with a blur kernel is noninvertible. To tackle this problem, additional image priors, such as the global gradient distribution from clear images [7], are proposed. Some approaches use multiple images or additional visual cues [2, 20] to constrain the kernel estimation. (a) (b)
Locally adapted hierarchical basis preconditioning
- ACM Transactions on Graphics
, 2006
"... This paper develops locally adapted hierarchical basis functions for effectively pre-conditioning large optimization problems that arise in computer vision, computer graphics, and computational photography applications such as surface interpolation, optic flow, tone mapping, gradient-domain blending ..."
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Cited by 44 (6 self)
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This paper develops locally adapted hierarchical basis functions for effectively pre-conditioning large optimization problems that arise in computer vision, computer graphics, and computational photography applications such as surface interpolation, optic flow, tone mapping, gradient-domain blending, and colorization. By looking at the local structure of the coefficient matrix and performing a recursive set of variable eliminations, combined with a simplification of the resulting coarse level problems, we obtain bases better suited for problems with inhomogeneous (spatially varying) data, smoothness, and boundary constraints. Our approach removes the need to heuristi-cally adjust the optimal number of preconditioning levels, significantly outperforms previous approaches, and also maps cleanly onto data-parallel architectures such as modern GPUs. [ Errata in (8) and (9) fixed October, 2007]
