Abstract-Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.

Abstract-Image restoration is an important issue in high level image processing which deals with recovering of an original and sharp image using a degradation and restoration model. During image acquisition process degradation occurs. Image restoration is used to estimate the original image from the degraded data. Aim of this research paper is to provide a concise overview of most useful restoration models. Using the proposed approach the features of the neighboring pixels are calculated and on basis of these features image is restored. Keywords-Include at least 5 keywords or phrases I. INTRODUCTIONImage restoration is a main part of digital image processing, including research on restoration algorithms, and programming of some specific image processing problems. In the processes of image generation, transmission and recording, some distortion and various-degree degradation occur inevitably. The purpose of image restoration is to remove degradation factors of degraded images, and recover it as far as possible. The quality of reconstructed images is judged not only by human subjective feeling, but also by some objective measures, such as squared error between reconstructed image and original image etc.There are many degradation sources in the image system. Some degradation factors affect only some individual gray scale of the image, while some degradation factors blur a spatial area of the image. The former iscalled point degradation, and the latter is called spatial degradation. There are also degradations caused by digital, display, time, color, and chemical. This paper focuses on image restoration blurred by uniform linear motion.Photos of running cars andflying airplanes are blurred, which is caused by motion of photographed objects. Similarly, if capturing a static object from the running car, the photo is also blurred, which is caused by motion of the camera. In sum, the image is blurred if there is enough relative motion between camera and photographed object during photographing. The phenomenon is called motion blur. It is a common problem of image processing, so research on motion-blurred image restoration is a main subject of image restoration.Sondhi reviewed the techniques for digital restoration of images. Some examples of restoration were included to illustrate the methods discussed [1]. Fitton used Hough transform to extract linear features successfully from geoscientific images [2]. Many motion-blurred image restoration methods were proposed recent years, and the methods can be divided into two categories: methods in frequency domain and methods in spatial domain. Srivastava et al. proposed generalized partial differential equations based model to recover the original image from the blurred image in spatial domain [3]. Quan and Zhang builtup the degradation model of motion blurred star image. Wiener filter with optimal window technique was adopted to deal with the motion-blurred images and its performance in restoration of image was discussed [4]. Li and Zhan compared several recovery algorithms an...