Block Compressive Sampling and Wiener Curvelet Denoising Approach for Satellite Images

Abstract

Satellite optical system produces high-resolution images which deal with large volume of data. This imposes strain on embedded resources which require more memory and computing capacity. In classical satellite imaging system, conventional compression algorithms like JPEG were used. However, they are not very efficient in reducing the data rate. In order to overcome this, block compressive sensing (BCS) technique, reweighted sampling (RWS) are used. This technique provides block-by-block sampling continuously at a rate which is very much less than the Nyquist rate. Due to the interference with high frequency signal in the environment, noise is induced in the compressed data from the satellite while transmitting them to the ground station. Curvelet transform with Wiener filtering technique (CTWF) is used for significant denoising of the BCS data. Experimental results show that BCS along with denoising technique reproduces images with better PSNR values.