Comparative Study of Different Noise Models and Effective Filtering Techniques

Real world signals usually contain departures from the ideal signal that would be produced by our model of the signal production process. Such departures are referred to as noise. Noise arises as a result of unmodelled or unmodellable processes going on in the production and capture of the real signal. It may be caused by a wide range of sources, e. g. variations in the detector sensitivity, environmental variations, the discrete nature of radiation, transmission or quantization errors, etc. Digital Image processing system contains operators to artificially add noise to an image. Deliberately corrupting an image with noise allows us to test the resistance of an image processing operator to noise and assess the performance of various noise filters. Getting an efficient method of removing noise from the images, before processing them for further analysis is a great challenge for the researchers. The kind of the noise removal techniques to remove the noise depends on the type of noise present in the image. Best results are obtained if testing image model follows the assumptions and fail otherwise. In this paper, light is thrown on some important type of noise and a comparative analysis of noise removal techniques is done. This paper presents the results of applying different noise types to an image model and investigates the results of applying various noise reduction techniques.