Antialiasing recovery

Yang, Lei; Sander, Pedro V.; Lawrence, Jason; Hoppe, Hugues

doi:10.1145/1966394.1966401

Cited by 22 publications

(21 citation statements)

References 19 publications

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“…While Fattal [8] imposed strong priors based on edge statistics to smooth "stair step" edges, this type of approach still struggles in areas of texture. Methods like [9] for producing high quality antialiased edges from jagged input are inappropriate here, for reasons illustrated in Fig. 1.…”

Section: Related Workmentioning

confidence: 99%

Patch Based Synthesis for Single Depth Image Super-Resolution

Aodha

Campbell

Nair

et al. 2012

Computer Vision – ECCV 2012

163

196

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Abstract. We present an algorithm to synthetically increase the resolution of a solitary depth image using only a generic database of local patches. Modern range sensors measure depths with non-Gaussian noise and at lower starting resolutions than typical visible-light cameras. While patch based approaches for upsampling intensity images continue to improve, this is the first exploration of patching for depth images. We match against the height field of each low resolution input depth patch, and search our database for a list of appropriate high resolution candidate patches. Selecting the right candidate at each location in the depth image is then posed as a Markov random field labeling problem. Our experiments also show how important further depth-specific processing, such as noise removal and correct patch normalization, dramatically improves our results. Perhaps surprisingly, even better results are achieved on a variety of real test scenes by providing our algorithm with only synthetic training depth data.

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Section: Related Workmentioning

confidence: 99%

Patch Based Synthesis for Single Depth Image Super-Resolution

Aodha

Campbell

Nair

et al. 2012

Computer Vision – ECCV 2012

163

196

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“…This is particularly important for thresholded images, whose response function is close to a step-function. Recently, Yang et al [34] suggested a method for restoring antialiased edges that suffered degradation from applying certain types of non-linear filters, such as those used in many NPR algorithms. While their approach may be efficiently implemented on a GPU, we propose an even simpler solution for the case of the XDoG operator.…”

Section: Anti-aliasingmentioning

confidence: 99%

XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization

Winnemöller

Kyprianidis

Olsen³

2012

Computers & Graphics

215

187

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Recent extensions to the standard difference-of-Gaussians (DoG) edge detection operator have rendered it less susceptible to noise and increased its aesthetic appeal. Despite these advances, the technical subtleties and stylistic potential of the DoG operator are often overlooked. This paper offers a detailed review of the DoG operator and its extensions, highlighting useful relationships to other image processing techniques. It also presents many new results spanning a variety of styles, including pencil-shading, pastel, hatching, and woodcut. Additionally, we demonstrate a range of subtle artistic effects, such as ghosting, speed-lines, negative edges, indication, and abstraction, all of which are obtained using an extended DoG formulation, or slight modifications thereof. In all cases, the visual quality achieved by the extended DoG operator is comparable to or better than those of systems dedicated to a single style.

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“…Ma and Xu [21] have applied anti-aliasing recovery (originally proposed by Yang et al [38]) for reducing fringe-like artifacts common in previous edit-propagation methods [2,19]. Chen et al [9] used a manifold approximation technique referred to as locally linear embedding (LLE) [28] to approximate the input image.…”

Section: Related Workmentioning

confidence: 99%

Sparse pixel sampling for appearance edit propagation

Yatagawa

Yamaguchi

2015

Vis Comput

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Edit propagation is an appearance-editing method using sparsely provided edit strokes from users. Although edit propagation has a wide variety of applications, it is computationally complex, owing to the need to solve large linear systems. To reduce the computational cost, interpolation-based approaches have been studied intensely. This study is inspired by an interpolation-based edit-propagation method that uses a clustering algorithm to determine samples. The method uses an interpolant, which approximates edit parameters with convex combinations of the samples. However, because the clustering algorithm generates samples that lie inside the set of pixels in a feature space, an interpolant with convex combinations does not allow for an exact reconstruction of the pixels outside the convex hull. To address this issue, this paper proposes a novel approximation model for interpolating image colors as well as edit parameters using affine combinations. In addition, this paper introduces sparse pixel sampling to determine the quantity and positions of samples and the weight coefficients of the affine combinations simultaneously. Sparse pixel sampling is performed by updating candidate pixels. Unnecessary pixels are discarded with compressive sensing, and new candidate pixels are greedily resampled following their approximation errors. This paper demonstrates that the B Tatsuya Yatagawa proposed model achieves better approximation in terms of both image colors and edit parameters, and discusses the properties of the proposed model with various experiments.

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Antialiasing recovery

Cited by 22 publications

References 19 publications

Patch Based Synthesis for Single Depth Image Super-Resolution

Patch Based Synthesis for Single Depth Image Super-Resolution

XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization

Sparse pixel sampling for appearance edit propagation

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