In image processing. In general, the noise in

In present digital world, imaging science has become an integral part of human life. Starting from medical to space research, everything has been based on image processing. Therefore, the capturing the correct image has become a very important issue. Beside this if an image is not captured properly or gets corrupted due to addition of noise, then there is a need of correct and efficient noise removal method.

  The image restoration is a very wast field and various type of advance research already have been done in this domain. However, when the method is complex to implement and produces a very low level of output after lots of iterations of calculation, then we really need an novel approach to produce good results from simple calculations and processing. In the digital image processing, a image is represented into a two-dimensional format with a finite set of digital values. These finite values are called as picture elements or pixels. Understanding the pixel concept is most of our proposed approach. Pixel describes programmable color on a computer display of each point of an image. In digital imaging, pixel is a physical point in a raster image, or the smallest addressable element in an all points addressable display device.

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In color image systems, a color is typically represented by three component intensities such as red, green, and blue. We have used the these components to identify the intensity of the pixels. cite{lougheed1985neighborhood} describes how can image be corrupted due to various kind of motion, noise or signal during the process of acquisition. To detect this motion or noise in the picture various method of restoration has been introduced in image processing. In general, the noise in the image is defined by the regions which are remarkably different i.

e. darker or shiner comparing with the pixel with the neighboring pixels. To overcome this problem author ?rstly, focused on the causes of blur or corrupted image, among various method of restoration, emphasizes the neighboring pixel method, developed an algorithm for the restoration of an image by using distance transformation and mean method. Robert M Lougheed have emphasized on solving identification problems primarily noticed in forensic medicine, or in the creation of weather maps from satellite images. This method give significant result on bitmap graphics format images that have been scanned in or shot with digital cameras.A good number of filtering schemes such as Wiener filtercite{hillery1991iterative}, Bilateral filter cite{zhang2008adaptive}, Bayseian based iterative method cite{richardson1972bayesian} already have been developed over time for noise reduction and image restoration.

The Median filter cite{chen1999tri} is one the filtering schemes that are widely used for noise reduction and also have applications in digital signal processing. However, Wiener filter does not work properly when the variance of the noisy image is high. Whereas, Baysesin Recursive filter becomes computationally very expensive. The main objective of this paper is to introduce an approach for image restoration, whose main aim is to make an image noise-free. The entire paper is organized in the followingsequence. In section-1, Literature review about image restoration has been proposed.

In section-2, the nearest neighbour method and mean method including algorithm has been introduced. In section-3, the result obtained for the implementation of algorithm in MATLAB has been presented with analysis. Finally, the paper concludes with conclusion and references.