2014 •
Prediction of DCT-based denoising efficiency for images corrupted by signal-dependent noise
Authors:
Sergey Krivenko, Vladimir V. Lukin, Benoit Vozel, Kacem Chehdi
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 a (...)
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. (Read More)
Sergey Krivenko, Vladimir Lukin, Benoit Vozel, Kacem Chehdi
2014 IEEE 34th International Scientific Conference on Electronics and Nanotechnology (ELNANO) ·
2014
Artificial intelligence |
Computer vision |
We have placed cookies on your device to help make this website and the services we offer better. By using this site, you agree to the use of cookies. Learn more