Satellite Image Enhancement using Optimized Wavelet Decomposition and Bicubic Interpolation
T.V. Hyma Lakshmi1, K.Ch. Sri Kavya2, T.Madhu3, Sarat K Kotamraju4
1T.V.Hyma Lakshmi, pursuing Ph.D, Koneru Lakshmaia Education Foundation, A. P. India.
2Dr. K. Ch. Sri Kavya, Professor in the Department of Electronics and Communication Engineering in KLEF.
3Dr.Tenneti Madhu, B.E. degree, University of Madras, India.
4Dr. K. Sarat Kumar, Visiting Professor, Department of Telecommunications (ICT),Asian Institute of Technology, Thailand.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4430-4434 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6829018520/2020©BEIESP | DOI: 10.35940/ijrte.E6829.018520
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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: Satellite Images (SI) play a vital role in various civilian and military applications for weather forecasting, monitoring of resources of the earth, environmental studies, observing natural disasters and natural calamities, etc. When these SI are used in military applications and almost all other applications for efficient study, the big challenge is its resolution. In wavelet transforms based satellite image enhancement techniques, choosing a proper wavelet transform plays a key role and vary with the image to image. To improve the resolution, a novel robust optimized wavelet decomposition and a bicubic interpolation-based satellite image enhancement method is proposed. In this method, the Stochastic Diffusion Search (SDS) algorithm is used to get the optimized wavelet decomposition of the image into different subbands and bicubic interpolation is used to improve the resolution. Image is decomposed using the optimized wavelet filter bank based on the SDS algorithm, decomposed sub-bands are interpolated with bicubic interpolation and inverse wavelet transform is applied to compose the interpolated sub-bands into a high-resolution image. The proposed method is tested on satellite images and other images also. Compared to the proposed method with the existing methods and proved that the proposed method is superior to existing methods and applicable to any type of image.
Keywords: Bicubic Interpolation, Discrete Wavelet Transform, Optimized Wavelet Transform, PSNR, UIQI.
Scope of the Article: Discrete Optimization.