Search

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]

The stack filter is the most systematic among the nonlinear filters, including the weighted median filter and the morphology filter. The authors undertook to expand the class of stack filters and to increase the number of degrees of freedom in the design. They employed a fuzzy Boolean function in which the output Boolean function takes a continuous value between “0” and “1,” and proposed a fuzzy stack filter defined by that function. Up to the present, the fuzzy median filter and the fuzzy center weighted median filter have been presented as the most fundamental and simplest fuzzy stack filters. This paper proposes a fuzzy weighted median filter in which the weighted median filter, which is a more general nonlinear filter, is given a fuzzy property through the stack filter. The fuzzy weighted median filter changes continuously from the weighted median filter to the weighted average filter by varying the setting of the fuzzy Boolean function. This implies that the fuzzy weighted median filter has a high noise elimination ability for both impulsive and Gaussian noise. The fuzzy weighted median filter can be designed independently for the weight function and the fuzzy Boolean function. This paper aims at a simple design for the weight function and the fuzzy Boolean function, without losing its general character. The weight is approximated by an S-shaped function with only the distance from the processed point as the parameter, and the fuzzy Boolean function is approximated by a sigmoid function. The design is reduced to the determination of four parameters, which is performed by the LMS algorithm. Through application examples, the properties and the effectiveness of the fuzzy weighted median filter are revealed. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(10): 40–49, 1999 [ABSTRACT FROM AUTHOR]