A local adaptive non‐linear algorithm for robust speech processing is proposed. The algorithm is based on calculation of the rank‐order statistics of an input speech signal over a sliding window. The algorithm is locally adaptive because it can vary the size and contents of a sliding window signal as well as an estimation function employed for recovering a clean speech signal from a noisy signal. The algorithm is able to improve the quality of a speech signal preserving its intelligibility and introducing only imperceptible musical noise. The performance of the adaptive algorithm for suppressing additive, impulsive and mixed noise in an input test speech signal is compared with that of existing speech enhancement algorithms in terms of several objective metrics.

A new motion estimation algorithm on the basis of a dynamic programming optimization operator (DPOO) is proposed. Motion estimation computation is formulated as a matching optimization problem of multiple dynamic images. A new operator that is a modification of dynamic programming recursion has been designed. This operator allows multiple implementations, and extends 1D optimization of the dynamic programming method to N-D optimization. Discrete Fourier transform based data level reduction for the motion estimation algorithm has been realized.

A new method for computing precise depth map estimates of 3D shape of a moving object is proposed. 3D shape recovery in motion stereo is formulated as a matching optimization problem of multiple stereo images. The proposed method is a heuristic modification of dynamic programming applied to two-dimensional optimization problem. 3D shape recovery using real motion stereo images demonstrates a good performance of the algorithm in terms of reconstruction accuracy.

We demonstrate the use of informational redundancy of signals and transforms for reducing the computational costs of signal processing. Four concrete examples of accelerated signal processing algorithms are presented to support the idea of purposive use of signal and transform redundancy for saving the computational costs. These are: an accelerated algorithm for Fourier spectral analysis, an accelerated algorithm for computing the signal local histograms, the quantized discrete Fourier transforms, and recursive implementation of arbitrary digital filters. The former two reduce computation time by exploiting signal redundancy. The latter two save processing time at the expense of the accuracy of representation of the corresponding signal transforms.