Open AccessSatellite Image Enhancement using Optimized Wavelet Decomposition and Bicubic Interpolation
Author(s) -
T.V.Hyma Lakshmi*,
K. Ch. Sri Kavya,
Dr.Tenneti Madhu,
K. Sarat Kumar
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6829.018520
Subject(s) - bicubic interpolation , artificial intelligence , interpolation (computer graphics) , wavelet , computer vision , computer science , wavelet transform , demosaicing , satellite , stairstep interpolation , wavelet packet decomposition , bilinear interpolation , image resolution , image (mathematics) , algorithm , pattern recognition (psychology) , image processing , multivariate interpolation , binary image , engineering , aerospace engineering
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.