A survey of RST invariant image watermarking algorithms
ACM Computing Surveys (CSUR)
Spatial adaptive Bayesian wavelet threshold exploiting scale and space consistency
Multidimensional Systems and Signal Processing
Watermarking oriented video modelling in the wavelet domain
Math'04 Proceedings of the 5th WSEAS International Conference on Applied Mathematics
Universal Steganalysis Using Multiwavelet Higher-Order Statistics and Support Vector Machines
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Using Sensor Noise to Identify Low Resolution Compressed Videos from YouTube
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
An improved camera identification method based on the texture complexity and the image restoration
Proceedings of the 2009 International Conference on Hybrid Information Technology
Detection of motion-incoherent components in video streams
IEEE Transactions on Information Forensics and Security
Mixed local-global criterion for image denoising in the wavelet domain
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Adaptive wavelet threshold for image denoising by exploiting inter-scale dependency
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Wavelet-based CR image denoising by exploiting inner-scale dependency
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Imaging sensor noise as digital X-ray for revealing forgeries
IH'07 Proceedings of the 9th international conference on Information hiding
Blind and passive digital video tamper detection based on multimodal fusion
ICCOM'10 Proceedings of the 14th WSEAS international conference on Communications
On the influence of denoising in PRNU based forgery detection
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Novel blind video forgery detection using markov models on motion residue
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Source video camera identification for multiply compressed videos originating from YouTube
Digital Investigation: The International Journal of Digital Forensics & Incident Response
Detecting re-captured videos using shot-based photo response non-uniformity
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Gradient-based Wiener filter for image denoising
Computers and Electrical Engineering
| Hi-index | 0.00 |
This paper deals with the application to denoising of a very simple but effective "local" spatially adaptive statistical model for the wavelet image representation that was previously introduced successfully in a compression context. Motivated by the intimate connection between compression and denoising, this paper explores the significant role of the underlying statistical wavelet image model. The model used here, a simplified version of the one proposed by LoPresto, Ramchandran and Orchard (see Proc. IEEE Data Compression Conf., 1997), is that of a mixture process of independent component fields having a zero-mean Gaussian distribution with unknown variances /spl sigma//sub s//sup 2/ that are slowly spatially-varying with the wavelet coefficient location s. We propose to use this model for image denoising by initially estimating the underlying variance field using a maximum likelihood (ML) rule and then applying the minimum mean squared error (MMSE) estimation procedure. In the process of variance estimation, we assume that the variance field is "locally" smooth to allow its reliable estimation, and use an adaptive window-based estimation procedure to capture the effect of edges. Despite the simplicity of our method, our denoising results compare favorably with the best reported results in the denoising literature.