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Telecommunications and Radio Engineering
SJR: 0.202 SNIP: 0.2 CiteScore™: 0.23

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

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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 of Technology, Signal Processing Laboratory, P. O. Box 553, FIN-33101, Tampere, Finland

ABSTRACT

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.


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DENOISING OF MULTICHANNEL IMAGES WITH REFERENCES
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