Modern remote sensing systems basically acquire images that are multichannel (dual- or multi-polarization, multi- and hyperspectral) where noise, usually with different characteristics, is present in all components. If noise is intensive, it is desirable to remove (suppress) it before applying methods of image classification, interpreting, and information extraction. This can be done using one of two approaches – by component-wise or by vectorial (3D) filtering. The second approach has shown itself to have higher efficiency if there is essential correlation between multichannel image components as this often happens for multichannel remote sensing data of different origin. Within the class of 3D filtering techniques, there are many possibilities and variations. In this paper, we consider filtering based on discrete cosine transform (DCT) and pay attention to two aspects of processing. First, we study in detail what changes in DCT coefficient statistics take place for 3D denoising compared to component-wise processing. Second, we analyze how selection of component images united into 3D data array influences efficiency of filtering and can the observed tendencies be exploited in processing of images with rather large number of channels.
Performance of denoising based on discrete cosine transform applied to multichannel remote sensing images corrupted by additive white Gaussian noise is analyzed. Images obtained by satellite Earth Observing-1 (EO-1) mission using hyperspectral imager instrument (Hyperion) that have high input SNR are taken as test images. Denoising performance is characterized by improvement of PSNR. For hard-thresholding 3D DCT-based denoising, simple statistics (probabilities to be less than a certain threshold) are used to predict denoising efficiency using curves fitted into scatterplots. It is shown that the obtained curves (approximations) provide prediction of denoising efficiency with high accuracy. Analysis is carried out for different numbers of channels processed jointly. Universality of prediction for different number of channels is proven.
Results of denoising based on discrete cosine transform for a wide class of images corrupted by additive noise are obtained. Three types of noise are analyzed: additive white Gaussian noise and additive spatially correlated Gaussian noise with middle and high correlation levels. TID2013 image database and some additional images are taken as test images. Conventional DCT filter and BM3D are used as denoising techniques. Denoising efficiency is described by PSNR and PSNR-HVS-M metrics. Within hard-thresholding denoising mechanism, DCT-spectrum coefficient statistics are used to characterize images and, subsequently, denoising efficiency for them. Results of denoising efficiency are fitted for such statistics and efficient approximations are obtained. It is shown that the obtained approximations provide high accuracy of prediction of denoising efficiency.
Similar blocks (patches) search plays an important role in image processing. However, there are many factors making
this search problematic and leading to errors. Noise in images that arises due to bad acquisition conditions or other
sources is one of the main factors. Performance of similar patch search might make worse dramatically if noise level is
high and/or if noise is not additive, white and Gaussian. In this paper, we consider the influence of similarity metrics
(distances) on search performance. We demonstrate that robustness of similarity metrics is a crucial issue for
performance of similarity search. Two models of additive noise are used: AWGN and spatially correlated noise with a
wide set of noise standard deviations. To investigate metric performance, five test images are used for artificially
inserted group of identical blocks. Metric effectiveness evaluation is carried out for nine different metric (including
several unconventional ones) in three domains (one spatial and two spectral). It is shown that conventional Euclidian
metric might be not the best choice which depends upon noise properties and data processing domain. After establishing
the best metrics, they are exploited within non-local image denoising, namely the BM3D filter. This filter is applied to
intensity images of the database TID2008. It is demonstrated that the use of more robust metrics instead of classical
ones (Euclidean) in BM3D filter allows improving similar block search and, as a result, provides better results of image
denoising for the case of spatially correlated noise.
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