An Applied Method of Gaussian Noise by Fuzzy Filter

Abstract

The world has witnessed a major change in the way computing systems are used, especially to process images. DIP is a prominent area of research in the field of information technology that helps in processing digital images captured through various modalities using computing algorithms. Image processing has revolutionized greately thanks to its versatile applications and scalability. It not only helps to enhance images for better human interpretation but also allows processing images for storage and retrieval to be used for different representation and analysis. DIP techniques are generally versatile, reliable and accurate and additional benefit of being easier to implement than analog forms. The application of DIP pervades through various domains such as biology , Robotics. DIP uses size, color, shape and texture of objects under consideration for knowledge discovery. Respective domain knowledge is an important aspect when studying DIP. Image processing methods are numerous and varied and are used as per requirement in every field it is applied in. Techniques that were computationally prohibitive are now becoming reasonable within a DIP environment. Detection of edges in an image is very important step towards understanding image features. Edges are often considered as object boundaries at image locations. So edge detection is extensively used in image segmentation when images are divided into areas corresponding to different objects. This can be specifically used for enhancing tumor area in mammographic images. Different methods are available like laplacian & Gaussian edge operator. Breast boundaries, pectoral region and tumor location can be seen clearly by this method. Image noise suppression is highly demanding approach in DIP. Impulse noise is one which creates problem in acquisition transmission and processing of images. When image is restored filter act on 2 major roles .(1) Classification or detection and (2) Reconstruction of images which is filtering. Reconstruction involves replacing of corrupted pixels by certain approximation technique