Vladimir P. Melnik

Digital adaptive robust algorithms for radar image filtering

Novel adaptive robust filtering algorithms applicable to radar image processing are proposed. They take into consideration the peculiarities of radar images and possess a good combination of properties: effective speckle suppression, impulsive noise removal, edge and detail preservation and low computational complexity. The advantages of these digital algorithms are demonstrated by simulated data and images obtained by airborne side-look non SAR radar.

Removing impulse bursts from images by training-based filtering

The characteristics of impulse bursts in remote sensing images are analyzed and a model for this noise is proposed. The model also takes into consideration other noise types, for example, the multiplicative noise present in radar images. As a case study, soft morphological filters utilizing a training-based optimization scheme are used for the noise removal. Different approaches for the training are discussed. It is shown that these techniques can provide an effective removal of impulse bursts. At the same time, other noise types in images, for example, the multiplicative noise, can be suppressed without compromising good edge and detail preservation. Numerical simulation results, as well as examples of real remote sensing images, are presented.

Local activity indicators for hard-switching adaptive filtering of images with mixed noise

We present a quantitative analysis of several local activity indicator properties and provide comparisons between them. We illustrate that the considered local activity indicators can be applied as adaptation parameters to locally adaptive hard-switching processing (filtering) of images. The cases where images are corrupted with additive or multiplicative noise having different probability density functions and, possibly, spikes are considered. The advantages of the designed adaptive hard-switching filtering algorithms are discussed and demonstrated for simulated data. Recommendations concerning the selection of local activity indicators and threshold values are given.

Block-median pyramidal transform: analysis and denoising applications

A nonlinear multiscale pyramidal transform based on nonoverlapping block decompositions using the median operation and a polynomial approximation is considered. It is shown that this structure can be useful for denoising of oneand two-dimensional (1-D and 2-D) signals. Various denoising techniques are analyzed, including methods based on spatially adaptive thresholding and partial cycle-spinning algorithms. An analytical method for deriving the distribution function of the transform coefficients is also presented. This, in turn, can be used for the selection of thresholds for denoising applications.

Local transform-based denoising for radar image processing

We consider the problems of speckle removal in radar images. The proposed filtering techniques make use of local DCT denoising based on adaptive thresholding or homomorphic transformation. Two-stage DT denoising procedure in combination with local statistic filtering are also introduced and analyzed. The effectiveness of the proposed speckle removal algorithms is demonstrated by means of numerical simulations.

The use of sample selection probabilities for stack filter design

We propose a procedure for stack filter design that takes into consideration the filters sample selection probabilities. A statistical optimization of stack filters can result in a class of stack filters, all of which are statistically equivalent. Such a situation arises in cases of nonsymmetric noise distributions or in the presence of constraints. Among the set of equivalent stack filters, our method constructs a statistically optimal stack filter whose sample selection probabilities are concentrated in the center of its window. This leads to improvement of detail preservation.

Data fusion and processing for airborne multichannel system of radar remote sensing: methodology, stages, and algorithms

Methodology and stages of data processing in multichannel airborne radar imaging systems are considered. It is shown that data fusion in such systems requires special techniques, algorithms, and software for image processing and information retrieval. Some approaches and methods are proposed. The results are demonstrated for simulated and real images.

Two-stage radar image despeckling based on local statistic Lee and sigma filtering

The peculiarities of radar images and the problems of their filtering are considered. A two-stage procedure of radar image despeckling based on successive application of the local statistic Lee and sigma filters is proposed. The recommendations concerning filter parameter selection are presented. The performance characteristics of the proposed procedure are evaluated for a set of test artificial images. It is shown that the two-stage despeckling can be successfully applied to both images formed by side look aperture radar (SLAR) or synthetic aperture radar (SAR). An available trade-off of filter basic properties is provided. The examples for real data demonstrating the proposed procedure efficiency and benefits are also given.

Iterative block-median pyramid transform-based denoising

A nonlinear block-median pyramidal transform has been proposed. This transform is based on the iterative application of the median operation and linear Lagrange interpolation. The probability distribution of the transform coefficients has been analytically derived for i.i.d. input signals. The results of this statistical analysis are used for selecting the thresholds for denoising applications. Numerical simulation results are presented.

Image denoising using a block-median pyramid

A block-median pyramidal transform, based on the median operation over non-overlapping blocks and linear Lagrange interpolation, is considered for image denoising. In addition to the soft and hard thresholding schemes, other techniques are employed. Firstly, the partial cycle-spinning algorithm is used to achieve near translation invariance. Secondly, local adaptation in the transform domain is used for adjustment of parameters in accordance with spatial behavior of the image.