【文章內容簡介】 The edge can be detected effectively using Canny Edge Detection method. It differs from other edgedetection methods such as Sobel, Prewitt, Roberts, Log in that it uses two different thresholds foe detecting both strong and weak edges. Edge 5 provides a number of derivative (of the intensity is larger than threshold) estimators. The edge can be detected for checking whether there exists ringing effect in an input image. A) Canny Edge Detector Canny edge detection method finds edges by looking for local maxima of the gradient of f(x, y). The gradient is calculated using the derivative of a Gaussian Filter. The method uses two thresholds to detect strong and weak edges, and includes the weak edges in the output only if they are connected to strong edges. Therefore, this method is more likely to detect true weak edges. Steps involved in canny method: The image is smoothed using Gaussian Filter with a specified standard deviation, ? , to reduce noise The local gradient, g(x, y) and edge direction are puted at each point. The edge point determined give rise to ridges in the gradient magnitude image. This ridge pixels are then thresholds, T1amp。 T2, with T1T2. Ridge pixels with values greater than T2 are said to be‘ strong’ edge pixels. Ridge pixels with values between T1 amp。T2 are said to be ‘ weak’ edge pixels. B) Edgetaper for Ringing Effect: The ringing effect can be avoided using edge taper function. Edgetaper function is used to preprocess our image before passing it to the deblurring functions. It removes the high frequency dropoff at the edge of an image by blurring the entire image amp。 then replacing the center pixels of the blurred image with the original image. 4 Overall Architecture And Deblurring Algorithm The following Fig. 2 represents the overall architecture of this paper. The original image is degraded or blurred using degradation model to produce the blurred image. The blurred image should be an input to the Deblurring algorithm. Various algorithms are available for deblurring. In this paper, we are going to use Blind Deconvolution Algorithm. The result of this algorithm produces the deblurring image which can be pared with our original image[5]. 6 Overall Architecture A) Blind Deconvolution Algorithm: Blind Deconvolution Algorithm can be used effectively when no information of distortion is known. It restores image and PSF simultaneously. This algorithm can be achieved based on Maximum Likelihood Estimation (MLE) [6]. Algorithm for Deblurring: Input: Blurred image ‘ g’ Initialize number of iterations ‘ i’ Initial PSF ‘ h’ Weight of an image ‘ w’ % pixels considered for restoration a=0 (default) %Array corresponding to additive noise Procedure – II If PSF is not known then Guess initial value of PSF Else Specify the PSF of degraded image Restored Image f’= Deconvolution (g, h, i, w, a) End Procedure – II 7 5 Sample Rrsults The below images represent the result of degradation model using Gaussian blur. First image represented the original image and its edge can be estimated by Canny Edge detection method. Original Image The edge detection can be applicable to Gray Image. Therfore the origianl RGB image can be converted to gray image. After that Canny Edge Detection is applied for getting the Edges of the original image. 8 Edges of original Image The original can be blurred usi