Impulse noise removal from images is one of the most important concerns in digital image processing. Noise must be removed in a way that the main and important information of image is kept. Traditionally, the median filter has been the best way to deal with impulse noise; however, the image quality obtained in high noise density is not desirable. The aim of this paper is to propose an algorithm in order to improve the performance of adaptive median filter to remove high density impulse noise from digital images. The proposed method consists of two main stages of noise detection and noise removal. In the first stage, noise detection includes two global and local phases and in the second stage, noise removal is also done based on a two-phase algorithm. Global noise detection is done by a pixel classification approach in each block of the image and local noise detection is performed by automatically determining two threshold values in each block. In the noise removal stage only noise pixels detected from the first stage of the algorithm are processed by estimating noise density and applying adaptive median filter on noise-free pixels in the neighborhood. Comparing experimental results obtained on standard images with other proposed methods proves the success of the proposed algorithm.

Morphological faltering is a useful technique for the processing and analysis of binary and gray scale images. The extension of morphological techniques to color images is not a straightforward task because this extension stems from the multivariate ordering problem. Since multivariate ordering is ambiguous, existing approaches have used known vector ordering schemes for the color ordering purpose. In the last decade, many different color morphological operators have been introduced in the literature. Some of them have focused on noise suppression purposes. However, none has shown good performance, especially on edgy regions. In this paper, new color morphological operators, based on a fuzzy principle component analysis, are proposed for noise removal. These operators employ statistical information (obtained by applying a fuzzy clustering algorithm on the color space) to achieve the desired results for the denoising application. The performance of the proposed operators is compared with recent morphological operators, reported in the literature, for denoising purposes and the superiority of the proposed method is shown.

Cardiovascular diseases are a major threat to the survival of human beings. An electrocardiogram is used in the diagnosis of heart diseases. But many types of noises are present to deter the quality of ECG signals. Thus, these noises must be filtered using a Digital filter like FIR (Finite Impulse Response) filter. There are different approaches to designing an FIR filter. One of the prominent ways is by using windows. There are other ways of designing the FIR filters but filtering using windows is one of the fastest ways of designing the FIR filters. In this paper different orders to filter has been analyzed and the best order to filter the ECG is found. Using that order different windows of FIR filter has been designed using Xilinx System Generator which is used to filter the noisy ECG signal. Also, various windows have been compared for filtering noises in ECG like Baseline Wander noise and Muscle Artifacts noise. The best window among all the windows is found out using MATLAB Simulink.

In this paper, we propose an efficient noise robust edge detection technique based on odd Gaussian derivations in the wavelet transform domain. At first, new basis wavelet functions are introduced and the proposed algorithm is explained. The algorithm consists of two stage. The first idea comes from the response multiplication across the derivation and the second one is pruning algorithm which improves fake edges. Our method is applied to the binary and the natural grayscale image in the noise-free and the noisy condition with the different power density. The results are compared with the traditional wavelet edge detection method in the visual and the statistical data in the relevant tables. With the proper selection of the wavelet basis function, an admissible edge response to the significant inhibited noise without the smoothing technique is obtained, and some of the edge detection criteria are improved. The experimental visual and statistical results of studying images show that our method is feasibly strong and has good edge detection performances, in particular, in the high noise contaminated condition. Moreover, to have a better result and improve edge detection criteria, a pruning algorithm as a post processing stage is introduced and applied to the binary and grayscale images. The obtained results, verify that the proposed scheme can detect reasonable edge features and dilute the noise effect properly.