Pertti Koivisto

Shape preservation criteria and optimal soft morphological filtering

New criteria for shape preservation are presented. These criteria are applied in optimizing soft morphological filters. The filters are optimized by simulated annealing and genetic algorithms which are briefly reviewed. Situations, where this kind of criteria give better results compared to the traditional MAE and MSE criteria, are illustrated.

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.

Training-based optimization of soft morphological filters

Soft morphological filters form a large class of nonlinear filters with many desirable properties. However, few design methods exist for these filters. This paper demonstrates how optimization schemes, simulated annealing and genetic algorithms, can be employed in the search for soft morphological filters having optimal performance in a given signal processing task. Furthermore, the properties of the achieved optimal soft morphological filters in different situations are analyzed.

Optimal soft morphological filtering under breakdown probability constraints

A robust filtering method with good detail preservation properties is a desideratum in many signal processing applications. Unfortunately, it is typically difficult to design a filter having these two properties simultaneously. This paper gives a method to optimize filters by giving different penalties for deviations from the robustness and from the detail preservation requirements. It is illustrated that by relaxing the robustness requirement a little, the optimal filter can obtain significantly improved detail preservation properties.

Design of weighted order statistic filters by training-based optimization

This paper demonstrates how weighted order statistic filters can be designed using training-based optimization. The design method utilizes supervised learning with simulated annealing as the learning rule. In addition, the efficiency and flexibility of the presented method are studied through experiments.

Breakdown probabilities of recursive stack filters

A method for calculating breakdown probabilities of recursive stack filters is presented. The method is based on Markov chain models and on an interpretation of a noisy signal as a binary signal. As an example, the breakdown probabilities of recursive median filters are calculated.

Applications and properties of sigma and mean filters with adaptive window size

The sigma and mean filters with adaptive window size are proposed. The sigma filter with adaptive window size is intended for processing radar images with multiplicative noise and the main goal in its design is to improve the noise suppression effectiveness for homogeneous regions of images. The mean filter with adaptive window size can be applied for relief recovery when one initially has the isogram map and its primary approximation by constant level regions. The performance of the proposed filters is tested for simulated images and then analyzed for real data.

Removal of impulse bursts in satellite images

Characteristics of impulse bursts in satellite images are analyzed and methods for burst removal are considered. Artificial compact burst model is proposed and test images are created. An advanced multipass algorithm for the detection and removal of compact bursts in the presence of both additive and multiplicative noise is proposed. The efficiency of the algorithm is evaluated quantitatively using the artificial test images and visually using the artificial test images and real radar and optical satellite images. It is shown through experiments that the proposed method removes impulse bursts efficiently while preserving information

Modified vector sigma filter and its application to color and multichannel remote sensing radar image processing

A novel algorithm based on the sigma filter for processing multicomponent images is proposed. The noise suppression ability of the proposed vector filtering algorithm is better than, e.g., that of the standard sigma filter. Moreover, the added modifications make the filter able to remove impulsive noise. The proposed vector filter takes into account the mutual correlation between image components and preserves object edges and fine details even when the contrasts of the component images of multichannel data are low. The comparative analysis of filter performance is done both visually and using several quantitative criteria. Both simulated and real color and multichannel radar images are studied. It is shown that the modified vector sigma filter outperforms many component and vector filters. Two modifications are considered -- for cases of additive and multiplicative noise. Examples of the filter performance for processing real images formed by multipolarization/multifrequency side-look aperture radars are presented.

Optimal compositions of soft morphological filters

Soft morphological filters form a large class of nonlinear filters with many desirable properties. However, few design methods exist for these filters and in the existing methods the selection of the filter composition tends to be ad-hoc and application specific. This paper demonstrates how optimization schemes, simulated annealing and genetic algorithms, can be employed in the search for optimal soft morphological filter sequences realizing optimal performance in a given signal processing task. This paper describes also the modifications in the optimization schemes required to obtain sufficient convergence.