A Spatial Mean and Median Filter For Noise Removal in Digital Images

Abstract

ABSTRACT:In this project, Mean and Median image filtering algorithms are compared based on their ability to reconstruct noise affected images. The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image. In software, a smoothing filter is used to remove noise from an image. Each pixel is represented by three scalar values representing the red, green, and blue chromatic intensities. At each pixel studied, a smoothing filter takes into account the surrounding pixels to derive a more accurate version of this pixel. By taking neighbouring pixels into consideration, extreme "noisy" pixels can be replaced. However, outlier pixels may represent uncorrupted fine details, which may be lost due to the smoothing process. This project examines two common smoothing algorithms. These algorithms can be applied to one-dimensional as well as two-dimensional signals. For each of the two algorithms discussed, experimental results will be shown that indicate which algorithm is best suited for the purpose of impulse noise removal in digital color images. . KEYWORDS: Digital Image Processing, Mat lab. I.INTRODUCTIONIn image processing it is usually necessary to perform high degree of noise reduction in an image before performing higher-level processing steps, such as edge detection. The median filter is a non-linear digital filtering technique, often used to remove noise from images or other signals. The idea is to examine a sample of the input and decide if it is representative of the signal. This is performed using a window consisting of an odd number of samples. The values in the window are sorted into numerical order; the median value, the sample in the center of the window, is selected as the output. The oldest sample is discarded, a new sample acquired, and the calculation repeats. A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window. As part of this framework, several algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the algorithm switches between the outputs of mean filter. This paper is an enhancement to our earlier research with grey-scale images. In this paper, we propose two new detection-estimation based image filtering algorithms that effectively remove corrupted pixels with impulsive noise in digital color images. The existing methods for enhancing corrupted color images typically possess inherent problems in computation time and smoothing out edges because all pixels are filtered. Our proposed algorithms first classify corrupted pixels in each channel or in each pixel. Because marginal or vector median filtering is only performed for the classified pixels, the process is computationally efficient, and edges ...

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