A Survey on Filtering Techniques

This paper is a survey on different filtering techniques to achieve noise removal in image segmentation. In order to increase the efficiency of the segmentation process, only a part of the database need to be searched. For this searching process filtering techniques can be recommended. Filtering can be termed here as a removal of noises in the captured images. Image processing is basically the use of computer algorithms to perform image processing on digital images. Digital image processing has many significant advantages over analog image processing. Images are often degraded by noises. Noise can occur during image capture, transmission, etc. Noise removal is an important task in image processing. In general the results of the noise removal have a strong influence on the quality of the image processing technique. Image processing allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing of images. One of the most popular methods is wiener filter. In this paper four types of noise (Gaussian noise, Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by Mean filter, Median filter and Wiener filter, but till have so many drawbacks. So this paper presents FLICM based filtering techniques which will overcome the drawbacks and results for different filtering techniques also explained.