Median filter

About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.

Digital video compression system utilizing vector adaptive transform

Abstract: A video compression system comprises a pre-processing section (102), and encoder (106), and post-processing section (114). The pre-processing section (102) employs a median decimation filter (122) which combines median filtering and decimation process. The pre-processing section (102) also employs adaptive temporal filtering and content adaptive noise reduction filtering to provide images with proper smoothness and sharpness to match the encoder characteristics. The encoder (106) employs a two pass look-ahead allocation rate buffer control scheme where the numbers of bits allocated and subsequently generated for each block may differ. In the first pass, the means square error for each block is estimated to determine the number of bits assigned to each block in a frame. In the second pass, the degree of compression is controlled as a function of the total number of bits generated for all the preceding blocks and the sum of the bits allocated to such preceding blocks.

A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise

Abstract: In this letter, we propose a new adaptive weighted mean filter (AWMF) for detecting and removing high level of salt-and-pepper noise. For each pixel, we firstly determine the adaptive window size by continuously enlarging the window size until the maximum and minimum values of two successive windows are equal respectively. Then the current pixel is regarded as noise candidate if it is equal to the maximum or minimum values, otherwise, it is regarded as noise-free pixel. Finally, the noise candidate is replaced by the weighted mean of the current window, while the noise-free pixel is left unchanged. Experiments and comparisons demonstrate that our proposed filter has very low detection error rate and high restoration quality especially for high-level noise.

Advanced Digital Signal Processing And Noise Reduction

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Block-based noise estimation using adaptive Gaussian filtering

Abstract: This paper proposes a fast noise estimation algorithm using a Gaussian filter. It is based on block-based noise estimation, in which an input image is assumed to be contaminated by the additive white Gaussian noise and a filtering process is performed by an adaptive Gaussian filter. Coefficients of a Gaussian filter are selected as functions of the standard deviation of the Gaussian noise that is estimated from an input noisy image. For estimation of the amount of noise (i.e., standard deviation of the Gaussian noise), we split an image into a number of blocks and select smooth blocks that are classified by the standard deviation of intensity of a block, where the standard deviation is computed from the difference of the selected block images between the noisy input image and its filtered image. In the experiments, the performance of the proposed algorithm is compared with that of the three conventional (block-based and filtering-based) noise estimation methods. Experiments with several still images show the effectiveness of the proposed algorithm. The proposed noise estimation algorithm can be efficiently applied to noise reduction in commercial image - or video-based applications such as digital cameras and digital television (DTV) for its performance and simplicity.

Generalized multichannel image-filtering structures

Abstract: Recent works in multispectral image processing advocate the employment of vector approaches for this class of signals. Vector processing operators that involve the minimization of a suitable error criterion have been proposed and shown appropriate for this task. In this framework, two main classes of vector processing filters have been reported in the literature. Astola et al. (1990) introduce the well-known class of vector median filters (VMF), which are derived as maximum likelihood (ML) estimates from exponential distributions. Trahanias et al. (see ibid., vol.2, no.4, p.528-34, 1993 and vol.5, no.6, p.868-80, 1996) study the processing of color image data using directional information, considering the class of vector directional filters (VDF). We introduce a new filter structure, the directional-distance filters (DDF), which combine both VDF and VMF in a novel way. We show that DDF are robust signal estimators under various noise distributions, they have the property of chromaticity preservation and, finally, compare favorably to other multichannel image processing filters.