49843608-Complete-thesis-Report-merged

The use of non linear filters for both noise

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neighbors, and to derive the upper and lower bounds of the homogeneity level. The use of non linear filters for both noise correction and image preservation is also suggested in [27]. Median based filters are modified for detail-preservation images Mansoor et al. introduce an iterative edge preserving filtering technique using the blur metric. Noisy pixels have been categorized into edge and non edge pixels and different filtering schemes are applied. Stefan et al. presented a fuzzy two-step color filter for the reduction of impulse noise. This filter utilizes the fuzzy gradient values and fuzzy reasoning for the detection of noisy pixels. Mansoor et al [28] developed a recursive filter images which are highly corrupted by impulse noise. This filter estimates the noise level which is required to obtain the filter parameters. All these filters were specifically designed to reduce impulse noise [29]. The performance of the median filter in removing Gaussian noise is inadequate. This drawback is overcome with some success by employing another nonlinear filter technique which makes use of moving average filters (MAV). The concept behind a standard moving average filter is to replace its central pixel by the average value of its predefined neighborhood. One of the major issues in removing Gaussian noise is to differentiate between noise and edges. Various attempts have been made in the past to solve this problem. Fuzzy derivatives are used for task in [30]. The GOA filter was designed for reducing Gaussian type noise by estimating a fuzzy gradient in each direction so as to distinguish the local variation due to noise from that of an image structure. Stefan et al [31]. Consider the fuzzy distance between color pairs as a weight to perform the weighted average filtering for the removal of the Gaussian noise in color images. Russo [32] proposes a method for Gaussian noise filtering that combines a nonlinear algorithm for detail preserving and smoothing of noisy data, and a technique for automatic parameter tuning based on noise estimation. A new filtering architecture adopting multi parameter piecewise linear (PWI) functions is devised for the restoration
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29 of the images corrupted by the Gaussian noise [33]. Xiaofen and Qigang [34] made use of perceptual classification rules to separate noise from other relevant features of image. In yet another interesting work [35], fuzzy smoothing of images for Gaussian as well as Impulse noise is achieved by combining the output of several filters termed as hybrid filters. Most of the methods presented in literature deal with gray scale images. It is possible to extend these techniques to color images component wise i.e. each component R, G, and B can be passed through a filter separately. Barring a few, most papers on noise removal have dealt with the individual component of RGB color space separately. In the case of Gaussian noise, where differentiating between noise and edges (i.e. finer details of image) is difficult, dealing with each component introduces artifacts in the de-noised image. Only a few research studies have actually explored the interaction between the
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