Abstract
Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree Complex Wavelet Transform (DT-CWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DT-CWT in this proposed enhancement technique. Inverse DT-CWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques.
Highlights
The application of satellite imagery is increases day by day due the development in the sensor technologies in weather forecasting, astronomy, geographical information
Gamma corrected adaptive knee transformation based on beta wavelet for satellite image enhancement is explained in [1]
Singular Value Decomposition (SVD) and DWT based on Gamma Correction for satellite image enhancement are explained in [5]
Summary
The application of satellite imagery is increases day by day due the development in the sensor technologies in weather forecasting, astronomy, geographical information. Gamma corrected adaptive knee transformation based on beta wavelet for satellite image enhancement is explained in [1]. Advanced block based dwt technique for contrast enhancement of satellite images is presented in [2]. Input image is decomposed into various sub bands using DWT. Singular Value Decomposition (SVD) and DWT based on Gamma Correction for satellite image enhancement are explained in [5]. Intensity transformation based low contrast satellite images are enhanced. There are four various sub bands are included while decomposes the input image i.e. LL, LH, HL and HH. LL sub-band information of gamma is passed via SVD and IDWT is applied to reconstruct the enhanced image. An input image is decomposed by DT-CWT to get high-frequency sub bands. The high-frequency sub bands and the low-resolution input image are interpolated. Two different DWT decompositions are used to calculate the complex transform using DTCWT
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