Denoising Of Satellite Images

We use images in our day to day life for keeping a record of information or merely to convey a message. There are a number of parameters which determine the quality of an image or a photograph most of which cannot be solved manually without the help of a computer whatsoever any image that has been captured represents a deteriorated version of the original image. However its clear that by any means we can never get the ideal image which is hypothetical as it is 100% accurate which is not possible. Our aim in image processing is to get the best possible image with minimum number of errors. In order to come to the conclusion of a certain task the correction of this deteriorated version is of optimal importance. Rectifying too much lighting effects, instance noising, geometrical faults, unwanted colour variations and blur are some of the important parameters we need to attend to in order to get a good and useful image. In this paper, the deterioration of images because of noising has been addressed. Noise is any undesired information which adversely affects the quality and content of our image. Primary factors responsible for creating noise in an image are the medium through which photograph is taken (climatic and atmospheric factors like pressure and temperature), the accuracy of the instrument used to take the photograph (for instance camera) and the quantization of data used to store the image. This noise can be removed by an image processing technique called Image restoration. Image restoration process is concerned with the reconstruction of the original image from a noisy one.That is it tries to perform an operation on the image as the inverse of the imperfections in the image formation system. Degraded image can be perfected by various processes which are actually the reverse of noising. These filtering techniques are very simple and can be applied very easily through software. Some filtering processes have better performance than the others. This depends on the type of noise the image has. These filters are used in a variety of applications efficiently in preprocessing module. In this paper, the restoration performance of Arithmetic mean filter, Geometric mean filter and Median filter have been analyzed. The performance of these filters is analyzed by applying it on satellite images which are affected by Impulse noise, Speckle noise and Gaussian noise. Since the satellite images are being corrupted by various noises, the satellite images are considered in this paper to analyze the performance of arithmetic mean filter, geometric mean filter and median filter. By observing the obtained results and PSNR value for various satellite images under different noises, we have recorded the following conclusion. • the median filter gives better performance for satellite images affected by impulse noise than arithmetic mean filter and geometric mean filter. •the arithmetic mean filter gives better performance for gaussian noise than median filter and geometric mean filters for all satellite images. •the arithmetic mean filter gives better performance for speckle noise than median filter and geometric mean filter for all satellite images. Median Filter is an image filter that is more effective in situations where white spots and black spots appear on the image. For this technique the middle value of the m×n window is considered to replace the black and white pixels.After white spots and black spots appear on the image, it becomes pretty difficult to find which pixel is the affected pixel. Replacing those affected pixels with AMF, GMF and HMF is not enough because those pixels are replaced by a value which is not appropriate to the original one. It is observed that the median filter gives better performance than AMF and GMF for distorted images. The performance of restoration filter can be increased further to completely remove noise and to preserve the edges of the image by using both linear and nonlinear filter together