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The authors describe a technique for selectively sharpening only edges that were blurred due to various causes without making interference factors such as random noise more conspicuous. The selective sharpening technique described in this paper is implemented as an iterative updating nonlinear algorithm obtained by discretizing a type of nonlinear reaction-diffusion time-evolution equations. The update terms of this algorithm consist of a quadratic nonlinear smoothing term, reaction term, and overshooting term. By performing quantitative evaluation experiments, the authors showed that the proposed technique has significantly better selective sharpness enhancement capabilities than existing sharpness enhancement techniques such as the peaking method. In addition, the authors introduced a processing parameter adaptive control method in the proposed technique and applied it to the suppression of breathing distortions in video sequences. Breathing distortions, which are video sequence distortions that often appear in old films, are time-varying blurring distortions that occur due to temporal variations of the focus. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 87(5): 33–47, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10086 [ABSTRACT FROM AUTHOR]

The authors have previously proposed a fuzzy stack filter that is defined by a fuzzy Boolean function. We have also presented a specific design method (called the fuzzy Boolean function determination method) for the most fundamental fuzzy stack filter, the FCWM (fuzzy center weighted median). By varying the fuzzy Boolean function, we can achieve both an arbitrary CWM filter and a weighted average value filter in which only the weights at arbitrary processing points are varied. Hence, in comparison with the CWM filter, our filter is better at eliminating Gaussian noise. In this paper, to improve the reconstruction accuracy of nonsteady state signals, such as the image signals, a data-dependent FCWM (DD-FCWM) is proposed anew. The data-dependent filter is an adaptive filter in which the filter parameters are varied in response to the nature of the data near the processing point (local information). In a DD-FCWM filter, the fuzzy Boolean function is varied by using local information. We demonstrate that the proposed DD-FCWM filter uses the local signal information to appropriately vary, not only the weights of the processing points, but also the filter mode (average value type or median type). Furthermore, by use of image processing examples, we demonstrate that highly accurate signal reconstruction can be attained. © 1998 Scripta Technica, Electron Comm Jpn Pt 3, 81(8): 27–40, 1998 [ABSTRACT FROM AUTHOR]

One of the highly efficient coding methods for motion pictures is to combine the motion-compensation interframe prediction and the vector quantization. Among such methods, the normalized vector quantization is to represent the motion-compensated interframe difference signal, to separate the average value and to normalize the amplitude. It has a merit in that the general codebook can be utilized. This paper shows that the approximation error does not always decrease in those conventional methods, even if the vector quantization procedure is iterated. As a solution to this problem, it is shown to be sufficient to employ the vector quantizer with a gain/shape structure. An elaboration is made to the codebook size to handle the input vector amplitude. Then the motion picture coding algorithm is constructed using the proposed adaptive gain/shape vector quantizer. By optimizing the coding parameters aiming at the low bit-rate coded transmission, practical temporal and spatial resolutions are obtained for TV conferences and TV phones. [ABSTRACT FROM AUTHOR]