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Publication . Conference object . 2014

Prediction Of DCT-Based Denoising Efficiency For Images Corrupted By Signal-Dependent Noise

Sergey Krivenko; Vladimir V. Lukin; Benoit Vozel; Kacem Chehdi;
English
Published: 15 Apr 2014
Publisher: HAL CCSD
Country: France
Abstract
This paper describes a simple and fast way to predict efficiency of DCT-based filtering of images corrupted by signal dependent noise as this often happens for hyperspectral and radar remote sensing. Such prediction allows deciding in automatic way is it worth applying denoising to a given image under condition that parameters of signal-dependent noise are known a priori or pre-estimated with appropriate accuracy. It is shown that denoising efficiency can be predicted not only in terms of traditional quality criteria as output MSE or PSNR but also, with slightly less accuracy, in terms of visual quality metrics and PSNR-HVS-M.
Subjects by Vocabulary

ACM Computing Classification System: ComputingMilieux_MISCELLANEOUS ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

Microsoft Academic Graph classification: Mathematics Pattern recognition Signal Artificial intelligence business.industry business Discrete cosine transform Image (mathematics) Computer vision A priori and a posteriori Video denoising Hyperspectral imaging Noise reduction Noise (signal processing)

arXiv: Computer Science::Computer Vision and Pattern Recognition Computer Science::Multimedia

Subjects

efficiency prediction, DCT, Remote sensing, Noise reduction, Noise, Fitting, Filtering, Accuracy, visual quality metrics, traditional quality criteria, signal-dependent noise, radar remote sensing, image denoising efficiency, hyperspectral sensing, discrete cosine transform, PSNR-HVS-M, discrete cosine transforms, image denoising, DCT-based filtering, MSE, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing

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