You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
8 February 2010A method for blind estimation of spatially correlated noise
characteristics
In design of many image processing methods and algorithms, it is assumed that noise is i.i.d. However, noise in real life
images is often spatially correlated and ignoring this fact can lead to certain problems such as reduction of filter
efficiency, misdetection of edges, etc. Thus, noise characteristics, namely, variance and spatial spectrum are to be
estimated. This should be often done in a blind manner, i.e., for an image at hand and in non-interactive manner. This
task is especially complicated if an image is textural. Thus, the goal of this paper is to design a practical approach to
blind estimation of noise characteristics and to analyze its performance. The proposed method is based on analysis of
data in blocks of fixed size in discrete cosine transform (DCT) domain. This allows further use of the obtained DCT
spectrum for denoising and other purposes. This can be especially helpful for multichannel remote sensing (RS) data
where interactive processing is problematic and sometimes even impossible.
The alert did not successfully save. Please try again later.
Nikolay N. Ponomarenko, Vladimir V. Lukin, Karen O. Egiazarian, Jaakko T. Astola, "A method for blind estimation of spatially correlated noise characteristics," Proc. SPIE 7532, Image Processing: Algorithms and Systems VIII, 753208 (8 February 2010); https://doi.org/10.1117/12.847986