Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. Fuzzy image processing [4]...

In this paper, we propose a decision-based, signal-adaptive median filtering algorithm for removal of impulse noise. Our algorithm achieves accurate noise detection and high SNR measures without smearing the fine details and edges in the image. The notion of homogeneity level is defined for pixel values based on their global and local statistical properties. The cooccurrence matrix technique is...

Digital image processing plays a key role in medical diagnosis. Medical images are obtained and analyzed to determine the presence or absence of abnormalities such as tumor, which is vital in understanding the type and magnitude of a disease. Unfortunately, medical images are susceptible to impulse noise during acquisition, storage and transmission. Hence, image de-noising is a primary precurso...

These papers propose a novel method for noise removal in the images. The impulsive noise takes gray level values from the image as minimum or maximum from the dynamic gray level range. In this paper Set notations are proposed to eliminate the noise pixels corrupted by low and high gray level values. The method also adopt median and is compared with obtained Set notation values from local neighb...

A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF), is proposed. A popular method for removing impulsive noise is a median filter whereas the weighted median filter and center weighted median filter were also investigated. ADWMF is based on weighted median filter. In ADWMF, instead of fixed weights, weightages of the filter are dynamically assigned with th...

The fact that makes image denoising a difficult task is uncertainties in the impulse noise. The most knowledge in dayflies is uncertainty and erratic, unfortunately it is similar to impulse noise. The mathematic implements for handling uncertainty mostly are probability theory and fuzzy mathematics. That means, among the uncertainties involved in impulse noise, the randomness and the fuzziness ...

Background and objective: In the recent years image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Medical applications have been so much affected by these techniques which some of them are embedded in medical instruments such as MRI, CT and other medical devices. Among these techniques, medical image enhancement algorithms...

This paper represents a new algorithm which uses a trimmed global mean filter with ROAD to remove random impulse noise. A two step algorithm is implemented in which the first step ensure detection of corrupted pixels in the degraded image and the second step replaces the degraded image with either the median of uncorrupted pixels in the selected window and if the selected window contains noisy ...

A recursive filter for effective suppression of impulse noise is presented in this paper. The proposed work estimates the noise corruption level and the position of impulses in the first stage. The appropriate filter parameters for a detail-preserving restoration of the corrupted pixels are determined based on these estimations. The filtering process assumes a Gaussian spatial profile in the ne...

Noise removal is one of the most important parts to get original image from highly corrupted image. The image can be corrupted with noise during transmission from noisy channel, sensors or due to some environmental conditions. This makes the image visually unpleasant. Impulsive noise may occur during transmission which highly corrupts the image. In this paper an algorithm is designed to remove ...