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In signal processing, a filter is a device or process that removes from a signal some unwanted component or feature. Filtering is a class of signal processing, the defining feature of filters being the complete or partial suppression of some aspect of the signal. Most often, this means removing some frequencies and not others in order to suppress interfering signals and reduce background noise. However, filters do not exclusively act in the frequency domain; especially in the field of image processing many other targets for filtering exist. Correlations can be removed for certain frequency components and not for others without having to act in the frequency domain. There are many different bases of classifying filters and these overlap in many different ways; there is no simple hierarchical classification. Filters may be: *linear or non-linear *time-invariant or time-variant, also known as shift invariance. If the filter operates in a spatial domain then the characterization is space invariance. * causal or not-causal: depending if present output depends or not on "future" input; of course, for time related signals processed in real-time all the filters are causal; it is not necessarily so for filters acting on space-related signals or for deferred-time processing of time-related signals. *analog or digital *discrete-time (sampled) or continuous-time *passive or active type of continuous-time filter *infinite impulse response (IIR) or finite impulse response (FIR) type of discrete-time or digital filter. ==Linear continuous-time filters== Linear continuous-time circuit is perhaps the most common meaning for filter in the signal processing world, and simply "filter" is often taken to be synonymous. These circuits are generally designed to remove certain frequencies and allow others to pass. Circuits that perform this function are generally linear in their response, or at least approximately so. Any nonlinearity would potentially result in the output signal containing frequency components not present in the input signal. The modern design methodology for linear continuous-time filters is called network synthesis. Some important filter families designed in this way are: *Chebyshev filter, has the best approximation to the ideal response of any filter for a specified order and ripple. *Butterworth filter, has a maximally flat frequency response. *Bessel filter, has a maximally flat phase delay. *Elliptic filter, has the steepest cutoff of any filter for a specified order and ripple. The difference between these filter families is that they all use a different polynomial function to approximate to the ideal filter response. This results in each having a different transfer function. Another older, less-used methodology is the image parameter method. Filters designed by this methodology are archaically called "wave filters". Some important filters designed by this method are: *Constant k filter, the original and simplest form of wave filter. *m-derived filter, a modification of the constant k with improved cutoff steepness and impedance matching. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Filter (signal processing)」の詳細全文を読む スポンサード リンク
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