Satellite imaging is being the most attractive source of information for the governmental agencies and the commercial companies in last decade. The quality of the images is very important especially for the military or the police forces to pick the valuable information from the details. Satellite images may have unwanted signals called as noise in addition to useful information for several reasons such as heat generated electrons, bad sensor, wrong ISO settings, vibration and clouds. There are several image enhancement algorithms to reduce the effects of noise over the image to see the details and gather meaningful information. Many of these algorithms accept several parameters from the user to reach the best results. In the process of denoising, there is always a competition between the noise reduction and the fine preservation. If there is a competition between the objectives then an evolutionary multi objective optimization (EMO) is needed. In this work, the parameters of the image denoising algorithms have been optimized to minimize the trade-off by using improved Strength Pareto Evolutionary Algorithm (SPEA2). SPEA2 differs from the other EMO algorithms with the fitness assignment, the density estimation and the archive truncation processes. There is no single optimal solution in a multi objective problems instead there is a set of solutions called as Pareto efficient. Four objective functions, namely Mean Square Error (MSE), Entropy, Structural SIMilarity (SSIM) and Second Derivative of the image, have been used in this work. MSE is calculated by taking the square of difference between the noise free image and the deniosed image. Entropy is a measure of randomness of the content of difference image. The lower entropy is the better. The second derivate of an image can be achieved by convolving the image with the Laplacian Mask. SSIM algorithm is based on the similarities of the structures on the noise free image and the structures of the denoised image. For the image enhancement algorithms, Insight Segmentation and Registration Toolkit (ITK) is selected. ITK is an open source project and it is being developed in C++ to provide developers with a rich set of applications for image analysis. It includes tens of image filters for the registration and segmentation purposes. In this work, Bilateral Image Filter is evaluated in the field of satellite imaging for the noise removal process. The evaluated filter receives two parameters from the user side within their predefined ranges. Here, SPEA2 algorithm takes the responsibility to optimize these parameters to reach the best noise free image results. SPEA2 algorithm was implemented in Matlab and executable files of image filter were called in Matlab environment. The results of the work were represented graphically to show the effectiveness of selected method.
This site uses Akismet to reduce spam. Learn how your comment data is processed.