Denoising of Remotely Sensed Images via Curvelet Transform and its Relative Assessment

To extract information from remotely sensed images for wide range of applications, visual analysis and interpretation are required. In this paper, the denoising of remotely sensed images based on Fast Discrete Curvelet Transform (FDCT) has been proposed. The Fast Discrete Curvelet Transform via Wrapping(WRAP) and Unequally-Spaced Fast Fourier Transform (USFFT) has been discussed. With its optimal image reconstruction capabilities, the curvelet outperforms the wavelet technique in terms of both visual quality and Peak Signal to Noise Ratio (PSNR). This paper focuses on the analysis of denoising the Linear Imaging Self Scanning Sensor III (LISS III) images, Advanced Very High Resolution Radiometer (AVHRR) images from National Oceanic and Atmospheric Administration 19 (NOAA 19), METOP satellites for the Tirupati region, Andhra Pradesh, India. Numerical illustrations demonstrated that this method is highly effective for denoising the satellite images. (C) 2016 The Authors. Published by Elsevier B.V.