Interframe DPCM with robust median-based predictors for transmission of image sequences over noisy channels.

A new image sequence coding technique based on robust median-based predictors is presented for the transmission of image sequences over noisy channels. We analyze the robustness of median-based predictors against channel errors. A heuristic algorithm for the design of a robust predictor from a given median-based predictor is presented. It is shown that with small modifications in terms of a necessary requirement for a median-based predictor to be robust against channel errors, the robustness of a given median-based predictor can be considerably improved. Simulations on a real image sequence show significant improvement over the conventional differential pulse code modulation (DPCM) at high bit error rate (BER) using this new technique. The technique does not increase the transmission rate. It is shown that the quality of reconstructed images obtained by robust median-based predictors can be further improved by postprocessing the image using a nonlinear detail-preserving noise-smoothing filter.

Nonlinear multivariate image filtering techniques.

In this paper, nonlinear multivariate image filtering techniques are proposed to handle color images corrupted by noise. First, we briefly review the principle of reduced ordering (R-ordering) and then define three R-orderings by selecting different central locations. Considering noise attenuation, edge preservation, and detail retention, R-ordering based multivariate filters are designed by combining the R-ordering schemes. To implement color image filtering more effectively, we develop them into a locally adaptive version. The output of the adaptive filter is the closest sample to a central location that is a weighted linear combination of the mean, the marginal median, and the center sample. As a result, we study an adaptive hybrid multivariate (AHM) filter consisting of the mean filter, the marginal median filter, and the identity filter. The performance of the two adaptive filtering techniques is compared with that of some nonadaptive ones. The examples of color image filtering show that the adaptive multivariate image filtering gives a rather good performance improvement.

Evaluation of a computerized system for recognition of epileptic activity during long-term EEG recording.

A new method of recognition of epileptic activity using adaptive segmentation in EEG during long-term intensive monitoring was developed in Tampere. The performance of the system was validated and compared to the commercially available discharge recognition system of Gotman. Twelve approximately 30 min EEG segments recorded during intensive monitoring from 6 patients were analysed. On these EEG segments two EEG specialists marked the occurrence of epileptic activity independently. Later they re-evaluated any differences in their scoring. This consensus file was used as a reference in validating the performance of the two computer programs. We found that the program developed in Tampere detected discharge activity more often than the Gotman system. Both systems performed poorly in spike recognition. In the specificity of the recognized segments, the Gotman system was better.

Trends in nonlinear signal processing.

In this paper, we review several nonlinear filtering methods having desirable complementary properties to those of linear filters. Most of these methods are based on the median filter. Basic properties of these filters as well as some of their applications are reviewed.

Optimal parallel stack filtering under the mean absolute error criterion.

The authors extend the configuration of stack filtering to develop a new class of stack-type filters called parallel stack filters (PSFs). As a basis for the parallel stack filtering, the block threshold decomposition (BTD) is introduced, and its properties are investigated. The design of optimal PSHs under the mean absolute error (MAE) criterion is shown to be similar to the minimum MAE stack filtering theory. The only difference is that one needs now to design more than one stack filter that together construct an optimal PSF. As a result, while reviewing briefly the optimal stack filtering theory, they will put more efforts to demonstrate, via several examples, the improvement by switching from stack filtering to parallel stack filtering for the task of image noise removal.

A new class of detail-preserving filters for image processing.

A new class of median type filters for image processing is proposed. In the filters, linear FIR substructures are used in conjunction with the median operation. The root signals and noise attenuation properties of the FIR-median hybrid filters are analyzed and compared to representative edge preserving filtering operations. The concept of multilevel median operation is introduced to improve the detail preserving property of conventional median and the FIR-median hybrid filters. In the multilevel filters there exists a tradeoff between noise attenuation and detail preservation. The analysis and examples indicate that FIR-median hybrid filters preserve details better and are computationally much more efficient than the conventional median and the K-nearest neighbor averaging filters.