Stochastic wavelet-based image modeling using factor graphs and its application to denoising

Xiao, S.; Kozintsev, I.; Ramchandran, Kannan

doi:10.1109/icassp.2000.859271

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Having experimented with the de-noising method of [6] on images with artificial noise we find that, as in the example shown in Figure 2, edge and texture content is most often misclassified as noise. Moreover, due to the manipulation of a multi-scale wavelet representation, these errors are spread throughout a neighborhood around edge and texture content.…”

Section: Confidence Weighted Fingerprintsmentioning

confidence: 98%

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Confidence weighting for sensor fingerprinting

McCloskey

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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The use of photo-response non-uniformity (PRNU) has been proposed as the basis of a sensor fingerprint for common source camera identification. We perform tests of the PRNU-based fingerprint on a set of videos chosen to represent a wide range of potential inputs. Based on the results of these tests, we propose a confidence weighting scheme to address the problem of extracting a viable fingerprint from videos where high-frequency content (e.g. edges) persist at a given image location. We further show that the extended PRNU estimation algorithm with confidence weighting has improved performance on such problematic videos.

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Section: Confidence Weighted Fingerprintsmentioning

confidence: 98%

“…whereÎ k is the de-noised version of video frame I k , estimated using the method of Xiao et al [6]. The PRNU is a zero-mean array on the same M-by-N lattice as the sensor.…”

Section: Common Source Camcorder Identification By Prnu Estimationmentioning

confidence: 99%

Confidence weighting for sensor fingerprinting

McCloskey

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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On the transmission of a class of hidden Markov sources over Gaussian channels with applications to image communication

Kozintsev¹,

Ramchandran²

Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101)

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In this work we address the problem of transmission of a useful class of hidden Markov sources with Gaussian observations over a discrete-time power-constrained AWGN channel. Our source model approximates the wavelet representation of natural images, and is motivated by the recent successful modeling approaches in the state-of-the-art image compression and restoration methods. We derive information-theoretic performance bounds, and propose a simple yet efficient joint source-channel coding solution which combines analog and digital modes of transmission.Our results indicate the promise of the proposed approach to the problem of robust image communication.

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Statistical Image Modeling and Processing Using Wavelet-Domain Hidden Markov Models

Fan¹,

Xia²

2003

Electrical Engineering &Amp; Applied Signal Processing Series

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Stochastic wavelet-based image modeling using factor graphs and its application to denoising

Cited by 3 publications

References 9 publications

Confidence weighting for sensor fingerprinting

Confidence weighting for sensor fingerprinting

On the transmission of a class of hidden Markov sources over Gaussian channels with applications to image communication

Statistical Image Modeling and Processing Using Wavelet-Domain Hidden Markov Models

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