Gaussian Mixture Model and Lifting Wavelet Transformed Base Satellite Image Enhancement
T. V. Hyma Lakshmi1, T. Madhu2, K. Ch Sri Kavya3
1T.VHyma Lakshmi, Research Scholar, KLU ,AP, India.
2T Madhu, Principal, SIET, Naraspuram, A.P., India.
3KCh. Sri Kavya, Professor and Director, K L U, AP, India.
Manuscript received on 13 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 July 2019 | PP: 29-34 | Volume-8 Issue-2, July 2019 | Retrieval Number: A1224058119/19©BEIESP | DOI: 10.35940/ijrte.A1224.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: From the last few decades, Satellite images are being used widely in various applications like monitoring of forest areas, weather forecasting, polar bears counting, etc. In those applications to get more details of images efficiently, satellite images should be enhanced up to the required level as the images captured by the satellites are covered very large areas and those are very low-resolution images due to the high altitudes of satellites from the earth. We proposed a method of an image enhancement which includes both resolution enhancement and contrast enhancement. In this method, Stationary Wavelet Transform (SWT) in combination with Lifting Wavelet Transform (LWT) is used for image decomposition into low-frequency sub band images and high-frequency sub band images to separate smooth regions and sharp edges to interpolate regions and edges separately to reduce blurring effect in edges and noise in smooth regions. To get smoother details and sharper edges, Gaussian Mixture Model (GMM) is used for interpolation in resolution enhancement process and SWT with the combination of Contrasts Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement process. SWT in combination with LWT improves the resolution effectively and also minimizes the execution time drastically than existing methods due to the shift invariance of SWT and reduced computations in LWT and GMM interpolation results from sharper edges and smoother details. SWT is used in combination with CLAHE to enhance the contrast and mitigate the noise effects than existing methods. The proposed method gives superior results and compared with existing techniques with PSNR, Noise Estimation, and visual results.
Index Terms: Contrasts Limited Adaptive Histogram Equalization (CLAHE), Gaussian Mixture Model (GMM), Lifting Wavelet Transform (LWT), Peak Signal to Noise Ratio (PSNR), Stationary Wavelet Transform (SWT), Weighted Average.
Scope of the Article: Radar and Satellite