Gaussian Mixture Model and Lifting Wavelet Transformed Base Satellite Image Enhancement

From the last few decades, Satellite images are beingused widely in various applications like monitoring of forest areas,weather forecasting, polar bears counting, etc. In thoseapplications to get more details of images efficiently, satelliteimages should be enhanced up to the required level as the imagescaptured by the satellites are covered very large areas and thoseare very low-resolution images due to the high altitudes ofsatellites from the earth. We proposed a method of an imageenhancement which includes both resolution enhancement andcontrast enhancement. In this method, Stationary WaveletTransform (SWT) in combination with Lifting Wavelet Transform(LWT) is used for image decomposition into low-frequency subband images and high-frequency sub band images to separatesmooth regions and sharp edges to interpolate regions and edgesseparately to reduce blurring effect in edges and noise in smoothregions. To get smoother details and sharper edges, GaussianMixture Model (GMM) is used for interpolation in resolutionenhancement process and SWT with the combination of ContrastsLimited Adaptive Histogram Equalization (CLAHE) for contrastenhancement process. SWT in combination with LWT improvesthe resolution effectively and also minimizes the execution timedrastically than existing methods due to the shift invariance ofSWT and reduced computations in LWT and GMM interpolationresults from sharper edges and smoother details. SWT is used incombination with CLAHE to enhance the contrast and mitigatethe noise effects than existing methods. The proposed methodgives superior results and compared with existing techniques withPSNR, Noise Estimation, and visual results