Neural Network Based New Algorithm for Noise Removal and Edge Detection: A Survey

In this paper we have used different Filters and Methods for the filtration of the Image and to analyse that what exact difference it makes when it comes to detect the edge of the Image. The image processing part consists of image acquisition of noisy image. This part consists of several image-processing techniques. First, we introduce noise in the image at different density levels, then Bacteria Foraging Optimization Algorithm is used to calculate the Threshold value which is to be applied on each filter to remove noise from the image. Here we use Adaptive Median Filter, Haar Denoising Method and Hybrid Filter to remove noise. These Filters are then applied with BFO Algorithm and they are compared with one another which help us to calculate the parameters of noisy images. The parameters of working would be Noise level at different densities, Noise suppression rate, Mean Square Error and PSNR. Here Neural Network Approach is used which consists of feed forward and feed backward layers and at hidden to output layer, BFO Neural Network is used for classification of Image and finally edges are detected.