The Fast Adaptive Switching Trimmed Arithmetic Mean Filter (FASTAMF), concerned in this paper, has been proposed recently and is a very efficient technique from both noise suppression efficiency and computational cost point of view. Techniques using peer group concept įilters utilizing quaterions Schemes based on reduced vector ordering Those schemes can be categorized by the following families: There are numerous techniques of noisy pixel detection to be found in the literature. This way, not only the quality of output of restored image is preserved, but also a significant reduction of the computational cost is often achieved. Then, only those classified as noisy are further processed by the output estimation algorithm. The switching techniques use various approaches to determine if the processed pixel is corrupted or not. To address this issue, a significant improvement has been made by introduction of more sophisticated switching filters, which focus on the restoration of corrupted pixels only. Unnecessary processing of noise-free pixels results in inevitable degradation of the image quality. The main reason, that the efficiency of vector-ordering schemes is limited, lies in processing of every image pixel, regardless whether it is corrupted or not. Although this filter does not introduce any new colors to the processed image, there is no guarantee that the output pixel is itself noise-free, and thus, numerous solutions were developed to solve this problem and improve filtering performance. The output of VMF is the pixel from operational window for which the sum of distances to other samples from the window is minimized. One of the most basic filtering techniques, utilizing this ordering scheme, is the vector median filter (VMF). Those accumulated distances are then sorted and constitute the basis for further processing in various filtering algorithms. Many of them are based on a vector-ordering scheme, and use cumulative distances between samples in a window as dissimilarity estimates. Impulsive noise removal techniques are contextual processing schemes which estimate the channels of the processed pixel using information obtained from its neighborhood, represented by a sliding operational window. Therefore, the suppression of the impulsive noise is a critical step of image preprocessing. There are plentiful different techniques tailored for suppression of distinct type of noise, but most of them are vulnerable to occurrence of a impulsive noise, which introduces significant deviations of color image channel values. Therefore, noise suppression is one of the most frequently performed low-level image processing tasks. Natural or artificial electromagnetic interferences. There are various types of noise which affect acquisition and processing of digital color images. Rapid development of miniaturized high-resolution, low-cost image sensors, dedicated to operate in various lighting conditions, makes image enhancement and noise suppression to be very important operations of digital image processing.
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