Abo Bibliothek: Guest
Digitales Portal Digitale Bibliothek eBooks Zeitschriften Referenzen und Berichte Forschungssammlungen
Telecommunications and Radio Engineering
SJR: 0.202 SNIP: 0.2 CiteScore™: 0.23

ISSN Druckformat: 0040-2508
ISSN Online: 1943-6009

Volumes:
Volumen 78, 2019 Volumen 77, 2018 Volumen 76, 2017 Volumen 75, 2016 Volumen 74, 2015 Volumen 73, 2014 Volumen 72, 2013 Volumen 71, 2012 Volumen 70, 2011 Volumen 69, 2010 Volumen 68, 2009 Volumen 67, 2008 Volumen 66, 2007 Volumen 65, 2006 Volumen 64, 2005 Volumen 63, 2005 Volumen 62, 2004 Volumen 61, 2004 Volumen 60, 2003 Volumen 59, 2003 Volumen 58, 2002 Volumen 57, 2002 Volumen 56, 2001 Volumen 55, 2001 Volumen 54, 2000 Volumen 53, 1999 Volumen 52, 1998 Volumen 51, 1997

Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v75.i13.30
pages 1167-1191

DCT-BASED DENOISING IN MULTICHANNEL IMAGING WITH REFERENCE

V. V. Lukin
National Aerospace University (Kharkiv Aviation Institute), 17 Chkalov St., Kharkiv, 61070, Ukraine
S. K. Abramov
Department of Transmitters, Receivers and Signal Processing, National Aerospace University (Kharkiv Aviation Institute), 17 Chkalov St., Kharkiv, 61070, Ukraine
V. V. Abramova
National Aerospace University (Kharkiv Aviation Institute), 17, Chkalov St., Kharkiv, 61070, Ukraine
J. T. Astola
Tampere University of Technology, Signal Processing Laboratory, P. O. Box 553, FIN-33101, Tampere, Finland
Karen O. Egiazarian
Tampere University, Tampere, 33720, Finland

ABSTRAKT

A task of denoising of a component image of multichannel data is considered in this paper assuming that a reference (noise-free) image is available. We propose a denoising approach based on three-dimensional (3D) discrete cosine transform (DCT) applied in blocks. We show that a use of a reference image allows improving the denoising performance (measured by different quality metrics) although it depends on several factors such as a choice of the reference and the way it is pre-processed. One of the most important requirements to achieve a good performance is a similarity between to be processed and the reference images. A high cross-correlation between them is a necessary but not sufficient condition. These images should have also close dynamic range. If all these requirements are satisfied by an appropriate choice or by pre-processing of the reference, improvements of the metrics PSNR and PSNR-HVS-M can be up to 3...5 dB compared to the component-wise DCT-based image denoising. We also analyze and process real-life hyperspectral images and provide examples showing efficiency of filtering noisy component images using other components with high signal-to-noise ratios as references.


Articles with similar content:

DENOISING OF MULTICHANNEL IMAGES WITH REFERENCES
Telecommunications and Radio Engineering, Vol.76, 2017, issue 19
V. V. Lukin, S. K. Abramov, J. T. Astola, Karen O. Egiazarian, V. V. Abramova
DENOISING OF MULTICHANNEL IMAGES WITH NONLINEAR TRANSFORMATION OF REFERENCE IMAGE
Telecommunications and Radio Engineering, Vol.77, 2018, issue 9
V. V. Lukin, S. K. Abramov, Karen O. Egiazarian, V. V. Abramova
AN APPROACH TO PREDICTION OF SIGNAL-DEPENDENT NOISE REMOVAL EFFICIENCY BY DCT-BASED FILTER
Telecommunications and Radio Engineering, Vol.73, 2014, issue 18
Benoit Vozel, Kacem Chehdi, A. Naumenko, A. Rubel, V. V. Lukin, S. K. Abramov, J. T. Astola, Karen O. Egiazarian, S. S. Krivenko
AUTOMATIC ESTIMATION OF SPATIALLY CORRELATED NOISE VARIANCE IN SPECTRAL DOMAIN FOR IMAGES
Telecommunications and Radio Engineering, Vol.73, 2014, issue 6
Benoit Vozel, V. V. Lukin, S. K. Abramov, A. A. Roenko, V. V. Abramova
Real-Time Audio Watermarking
Telecommunications and Radio Engineering, Vol.65, 2006, issue 1-5
Mariko Nakano-Miyatake, Hector Manuel Perez-Meana, Jose Garcia-Hernandez