Fundamentals of Electronic Imaging Systems

Schreiber, William F.

doi:10.1007/978-3-662-00743-3

Cited by 22 publications

(15 citation statements)

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“…Unfortunately, this metric has only a loose correlation with percieved image quality [12]. Typical processing times for the single-pass algorithm are two seconds to enlarge a 100x100 image up to 200x200 pixels.…”

Section: Discussionmentioning

confidence: 99%

“…Recent attempts to improve on cubic spline interpolation [12,16,3] have met with limited success. Schreiber and collaborators [12] proposed a sharpened Gaussian interpolator function to minimize information spillover between pixels and optimize flatness in smooth areas. Schultz and Stevenson [13] have used a Bayesian method for super-resolution, but hypothesized the prior probability.…”

Section: Related Approachesmentioning

confidence: 99%

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Example-based super-resolution

Freeman

Jones

Pasztor

2002

IEEE Comput. Grap. Appl.

2,270

1,358

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Image-based models for computer graphics lack resolution independence: they cannot be zoomed much beyond the pixel resolution they were sampled at without a degradation of quality. Interpolating images usually results in a blurring of edges and image details. We describe image interpolation algorithms which use a database of training images to create plausible high-frequency details in zoomed images. Image pre-processing steps allow the use of image detail from regions of the training images which may look quite different from the image to be processed. These methods preserve fine details, such as edges, generate believable textures, and can give good results even after zooming multiple octaves.

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Section: Discussionmentioning

confidence: 99%

Section: Related Approachesmentioning

confidence: 99%

Example-based super-resolution

Freeman

Jones

Pasztor

2002

IEEE Comput. Grap. Appl.

2,270

1,358

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“…However, the reduction of noise using such techniques also often leads to the suppression of significant edge features. Since the presence of edges is perceptually related to sharpness and, hence, contrast [1], these approaches impact image quality significantly. There are smoothing methods that can also preserve edges, and Winkler et al [2] provide a good synopsis of such methods.…”

Section: Introductionmentioning

confidence: 99%

Anisotropic Filtering with Multi-resolution Edge Analysis and Connectivity Analysis

Jiang¹

2015

IJMLC

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Abstract-Generally, an edge-high frequency information in an image-would be filtered or suppressed after image smoothing. This results in noise attenuation, but the image also loses sharpness. This loss of sharpness can impact the usefulness of the processed image for further tasks. Our new anisotropic filtering performs multi-resolution edge analysis and connectivity analysis to make sure that only isolated edge information that represents noise gets filtered out, and the averaging process is within smooth regions not across edges, hence preserving the overall edge structure of the original image. Experimental results obtained from a suite of images but with different signal-noise-ratios (SNR), show that this method is robust to levels of noise and correctly preserves the edges, even for very extremely noisy condition.

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“…(3) corresponds to a squared error penalty for dierences between images. While this may be adequate for many applications, it is not a good model of the human visual system's response [57]. Our Bayesian framework can accomodate a dierent image error metric.…”

Section: Other Image Error Metricsmentioning

confidence: 99%

Exploiting the generic viewpoint assumption

Freeman¹

1996

Int J Comput Vision

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Thegeneric viewpointässumption states that an observer is not in a special position relative to the scene. It is commonly used to disqualify scene interpretations that assume special viewpoints, following a binary decision that the viewpoint was either generic or accidental. In this paper, we apply Bayesian statistics to quantify the probability of a view, and so derive a useful tool to estimate scene parameters. This approach may increase the scope and accuracy of scene estimates. It applies to a range of vision problems. We show shape from shading examples, where we rank shapes or reflectance functions in cases where these are otherwise unknown. The rankings agree with the perceived values. Vision, 20(3), 243-261, 1996 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. The \generic viewpoint" assumption states that an observer is not in a special position relative to the scene. It is commonly used to disqualify scene interpretations that assume special viewpoints, following a binary decision that the viewpoint was either generic or accidental. In this paper, we apply Bayesian statistics to quantify the probability of a view, and so derive a useful tool to estimate scene parameters. Generic variables can include viewpoint, object orientation, and lighting position. By considering the image as a (dierentiable) function of these variables, we derive the probability that a set of scene parameters created a given image. This scene probability equation has three terms: the delity of the scene interpretation to the image data; the prior probability of the scene interpretation; and a new genericity term, which favors scenes likely to produce the observed image. The genericity term favors image interpretations for which the image is stable with respect to changes in the generic variables. It results from integration over the generic variables, using a low-noise approximation common in Bayesian statistics. International Journal of ComputerThis approach may increase the scope and accuracy of scene estimates. It applies to a range of vision problems. We show shape from shading examples, where we rank shapes or reectance functions in cases where these are otherwise unknown. The rankings agree with the perceived values.

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Fundamentals of Electronic Imaging Systems

Cited by 22 publications

References 0 publications

Example-based super-resolution

Example-based super-resolution

Anisotropic Filtering with Multi-resolution Edge Analysis and Connectivity Analysis

Exploiting the generic viewpoint assumption

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