SAR Image Enhancement Using GUM Algorithm

In the applications like SAR images, medical radiography, enhancing movie features and observing the planets it is necessary to enhance the contrast and sharpness of an image. We propose a generalized unsharp masking (GUM) algorithm using the exploratory data model as a unified framework. The proposed algorithm is designed to address three issues: 1) simultaneously enhancing contrast and sharpness by means of individual treatment of the model component and the residual, 2) reducing the halo effect by means of an edge-preserving filter, and 3) solving the out of range problem by means of log ratio and tangent operations. Using this algorithm user can adjust the two parameters the contrast and sharpness to have desired output. This report presents two methods for image enhancement. One method is adaptive unsharp masking. Its objective is to emphasize the medium-contrast details in the input image more than large-contrast details. Another method’s objective is to smooth the uniform area and sharpen the borders between them. Image Enhancement transforms images to provide better representation of the subtle details. It is an indispensable tool for researchers in a wide variety of fields including, SAR images, medical imaging, art studies, forensics and atmospheric sciences.