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In this research paper a super-resolution (SR) image is generated from low resolution satellite image. By using high frequency (HF) as intermediate stage, sharper image can be generated. Interpolation is performed between satellite portrait and HF sub bands through the discrete wavelet transform which preserves edge. Sparse signal is obtained by applying sparse mixing weights to low resolution (LR) image. Lanczos interpolation is used to avoid artifacts generated in an image. High resolution (HR) image is obtained by using all bands and inverse DWT is performed. Experimental results proves that proposed method has superior performance over existing methods in terms of intension criteria and skewed perception improving the image resolution. The quantitative PSNR (peak signal to noise ratio), RMSE (root mean square value), and CC (correlation coefficient) are calculated for satellite images. The proposed technique i.e., super resolution using wavelet domain interpolation with edge extraction and sparse representation (SR-WDIEE-SR) has got better values compared to DWT (Discrete wavelet transform).

Super Resolution of Satellite Images Using Wavelet Based Edge Extraction through Sparse Representation