A Optimized Analysis of Different Edge Detection methods in Image Restoration with Neuro-Fuzzy Technique

Abstract Image restoration is to improve the quality of the degraded image. It is used to recover an image from distortions to its original image. Image Deconvolution method is used as a linear image restoration problem where the parameters of the true image are estimated using the observed or degraded image and a known Point Spread Function (PSF). To improve quality of image Genetic algorithm is replaced by technique called BBO for the restoration of image for better quality of image . Genetic algorithm[2] is slower than Biogeography based optimization So Ant colony optimization algorithms based on the pheromone trail laying and follow behavior of real ants which use pheromones as a communication medium have been applied to many combinatorial optimization problems, ranging from quadratic assignment to protein folding or routing of vehicles .In this paper , ACO technique used to optimized the edge detection[1] in image restoration. There are various edge detection methods available to detect an edge of the image such as Sobel, Prewitt, Roberts, and Canny. The edges can be detected effectively using Ant colony Optimization method[8]. Edge 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 or not. Thus ACO is used to solve image processing problem with a reference to a new automatic restoration technique based on real-coded particle ant colony is proposed in this paper. And also use Neuro-Fuzzy algorithm to enhance the previous result. ANFIS tool is used to overcome the limitation of previous work.