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Elimination of mixed noise consisting of white Gaussian noise and impulsive noise is one of the important problems in image processing. In this paper, a data-dependent α-trimmed mean filter using backpropagation networks is proposed. In order to determine an appropriate value of α for each processing point, the pattern classification capability of the backpropagation networks is used. Further, by processing taking into account the edge portions, both edge preservation and mixed noise elimination are attained simultaneously. Finally, the effectiveness of the proposed method is verified by simulation. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(7): 30–40, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10061 [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a data-dependent weighted average filter (Video-DDWA: Video Data-Dependent Weighted Average) aimed at restoration of dynamic images deteriorated due to Gaussian additive noises. As proposed by the authors, this filter is based on the data-dependent processing, in which the filter weight is varied by multiple local information items derived from the data proximal to the processing point in a still image, with extension of this processing from a spatial filter to a temporal-spatial filter; and then the weight of adjacent frames is determined by detecting presence or absence of motion from the motion information such as new local information. There are several conventional methods of restoration of dynamic images that involve motion compensation with subsequent spatiotemporal filter processing, but they all have limitations as to the filter restoration performance due to noise-affected deterioration of the estimation accuracy of the motion vector. The proposed data-dependent filter using the motion information has among others the following advantages: (1) Because it involves detection of the motion degree at which noise influence is suppressed, it enables restoration of dynamic images featuring a high noise cancellation performance in still areas, without motion deterioration due to filter processing in motion areas—in other words, with processing that does not cause deterioration of movement and (2) the computing load is lower than that with motion-compensated filters. As compared to the conventional methods using motion compensation, this method enables attaining a high restoration performance not only for signals with a low S/N ratio at which the accuracy of the motion compensation estimation vector starts decreasing, but also for signals with a wide range of S/N ratios. The authors demonstrate with various application examples that this method is efficient for restoration of dynamic images. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 84(4): 1–10, 2001 [ABSTRACT FROM AUTHOR]

An important problem in image processing is noise removal that preserves edges in images degraded by white Gaussian noise. In this paper, we propose a data-dependent weighted mean filter using counter propagation networks. This method achieves high-level performance in the removal of white Gaussian noise while preserving the edges. Coefficients of the filter and features to change them are adjusted and decided by using a layered neural network and self-organizing network, respectively. This allows a robust filter to be obtained. Analysis of this network enables the derivation of a simpler filter structure. Using this fact, we show an example of analysis and simplification, and obtained a filter capable of processing at high speed by fewer computations. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 84(3): 42–51, 2001 [ABSTRACT FROM AUTHOR]

This paper proposes a novel type of edge-preserving smoothing filter to be applied to an image corrupted by impulsive and white Gaussian noise. This filter is based on a weighted mean filter having two kinds of coefficient for impulsive and white Gaussian noise. These coefficients can be varied adaptively by local features in the window. Fuzzy control laws that can be used for the implementation of the proposed filter are described. To reduce the computational burden, the fuzzy inference model proposed by Ichihashi and coworkers is employed. Finally, we demonstrate the effectiveness of the proposed technique by using results from a simulation. 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(10): 19–27, 1997 [ABSTRACT FROM AUTHOR]