18-08-2011, 10:58 AM
ADAPTIVE TECHNIQUES BASED HIGH IMPULSIVE NOISE DETECTION AND REDUCTION OF A DIGITAL IMAGE
ABSTRACT
As reducing impulse noise in a digital image is a very active research area in image processing, this paper proposes a novel algorithm for digital image impulsive noise detection and reduction based on adaptive nonlinear techniques which seems to be a boom in digital image restoration process. The main objective of this algorithm is to consider a particular digital image as input and make the preprocessing to remove the impulsive noise content by employing suitable adaptive nonlinear filter after identifying the impulsive noise of overall image. The proposed algorithm consists of two parts. First, identifying the type of noise present in the image as additive, multiplicative or impulsive by analysis of local histograms and secondly, denoising the detected impulsive noise by employing adaptive nonlinear filtering technique which comprises a process of adaptive noise identification of a corrupt pixel and filtering it by employing adaptive nonlinear filter. In this paper, a new adaptive noise identification and adaptive nonlinear filtering algorithm is described to detect and remove the impulsive noise. Noise present in the digital image should be removed in such a way that the important information of image should be preserved. A decision based nonlinear algorithm for elimination of impulsive noise in digital images has been described in this paper. In order to improve the performances of classical median filter, an adaptive nonlinear filter is proposed and results obtained have been compared. The proposed algorithm has been simulated on MATLAB GUI. A simulation result shows that the proposed algorithm effectively identifies and removes the high impulsive noise by preserving image originality compare to standard median filter. Keywords: Impulsive noise, additive noise, multiplicative noise, adaptive noise identification, adaptive nonlinear filter.
1. INTRODUCTION
Image processing is a field that continues to grow, with new applications being developed at an ever-increasing pace. It is a fascinating and exciting area to be involved in today with application areas ranging from the entertainment industry to the space program. One of the most interesting aspects of this information revolution is the ability to send and receive complex data that transcends ordinary written text. Visual information, transmitted in the form of digital images, has become a major method of communication for the 21st century. Noise modeling in images is greatly affected by capturing instruments, data transmission media, image quantization and discrete sources of radiation. Different algorithms are used depending on the noise model. Most of the natural images are assumed to have additive random noise, which is modeled as a Gaussian. Automated techniques for identification of image noise are of considerable interest, because once the type of noise is identified from the given image, an appropriate algorithm can then be used to de-noise it. Only a few researchers have addressed this issue to date. However, algorithms proposed in [1] are pretty complicated, because their main goal is to estimate the statistical parameters of the noise. In this paper, a comparison of two simple techniques for identification of the type of noise present in an image is proposed. It may be pointed out that denoising often constitutes a first step that is followed by other automated image processing operations that analyze the image and extract useful features from it. The performance of the follow-up operation is often adversely affected due to poor de-noising results obtained in the first step. Since poor de-noising often results from poor noise identification, a better noise identification technique is always preferred.
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