International Journal of Computer Applications (0975 – 8887) Volume 150 – No.12, September 2016 30 A Brief Review on Image Restoration Techniques Shilpa Rani Mtech Student CSE Department SBSSTC,Punjab Sonika Jindal Assistant Professor CSE Department SBSSTC,Punjab Bhavneet Kaur Assistant Professor CSE Department SBSSTC,Punjab ABSTRACT Image restoration is process of recovering the original image by removing noise and blur from image. Image blur is difficult to avoid in many situations like photography, to remove motion blur caused by camera shake, radar imaging to remove the effect of image system response, etc. Image noise is unwanted signal which comes in image from sensor such as thermal or electrical signal and Environmental condition such as rain, snow etc. Researchers have proposed many methods in this regard and in this paper we will examine and discuss different noise and blur models and restoration methods. Keywords Degradation model, image noise and blur model, restoration techniques, point spread function (PSF), peak signal to noise Ratio (PSNR). 1. INTRODUCTION Digital images are electronic snapshots of a scene, which composed of typically picture elements in a grid formation known as pixels. Each pixel holds a value which is quantized that represents the tone at a specific point. Images are obtained in areas ranging from everyday photography to astronomy, remote sensing, microscopy, medical imaging etc. Image restoration uses a priori knowledge of the degradation. It formulates and evaluates the objective criteria of goodness. The distortion in image can be modeled as noise or blur or a degradation function. Unfortunately all images are more or less blurry. This is due to the reason that there is a lot of interference in the camera as well as in the environment. Blurring of an image can be caused by many factors such as movement during the capture process, using wide angle lens ,using long exposure times, etc.[1-2]. 1.1 Degradation Model The degradation process can be viewed with the following system. The degraded function is low pass filter. f(x,y) Degradation Degraded image function g(x,y) h(x,y) Noise n(x,y) Fig 1: Degeadation Model The original input is a two-dimensional image f(x, y). This image is operated on the system h(x, y) and after the addition of noise n(x, y). One can obtain the degraded image g(x, y). Digital image restoration may be visualized as a process in which we try to obtain an approximation to f(x, y). The blurred image can be described with the following equation. [3] g(x,y) = h(x,y) * f(x,y) + n(x,y) (1) 1.2 Blur model 1.2.1 Gaussian blur It is type of image blurring filter which use Gaussian function for calculating transformation applied on each pixel. The equation of Gaussian function is G = 1 2 − 2 2 2 (2) Where x is distance from origin in horizontal axis and σ is standard deviation of Gaussian distribution. 1.2.2 Motion Blur Motion blur occur in image due to camera misfocus and change in angle during taking of picture. 1.2.3 Rectangular blur This is blurring in image with specific rectangular area. Blur in image can be identified at any part based on this it can be circular and rectangular. 1.2.4 Defocus blur Defocus blur occurs in image when camera is improperly focused on image. The resolution of image medium depends on amount of defocus. If there is more tolerance of image there is low resolution in image. For good resolution of image defocus in image should be minimize. 1.3 Noise model Noise is unwanted variation in image. It results in change in visibility of an image. Digital images which are related to digital signals are normally corrupted by many types of noise, from which some most occurring noise are Gaussian noise, Salt and pepper noise (Impulse noise), Uniform noise, Brownian Noise, Inverse f Noise. These noise are represented by following formulae. 1.3.1 Gaussian noise = 1 2 − 2 2 2 (3) Where g=gray level =mean =standard deviation 1.3.2 Salt and pepper noise (Impulse noise) = = "" = "" (4) 1.3.3 Uniform noise P = 1 − ≤≤ 0 (5)