An Advanced Neural Network based Method for Noise Removal and Edge Detection (INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY, 2013)
Edge detection is an important pre-processing step for any image processing application, object recognition and emotion detection. Edge detection is very helpful in case of noise free images. But in case of noisy images it is a challenging task.Noisy images are corrupted images. Their parameters are difficult to analyze and detect. In this research work different filters are used for the filtration of the image and to analyze that what exact difference it makes when it comes to detect t he edge of the image. It includes the comparative study of various image denoising filters. These Filters are then applied withBFO Algorithm and they are compared with one another which help to calculate the parameters of noisy images. The comparison parameters which have been taken into contrast are Peak Signal to Noise Ratio, Mean Square Error and Noise Suppression Rate.