De-noising remotely sensed digital imagery

This paper applies two recent methods to denoise remotely sensed images - wavelet based Markov Random Field (MRF) methods, Independent Component Analysis (ICA) and compares them with the standard Wiener filter. In order to facilitate the continued use of these methods in remote sensing the theory behind each method is discussed in detail. Subsequently they are applied to de-noising remotely sensed images. The efficiency of each algorithm is obtained by computing the signal to noise ratio (SNR) before and after de-noising. Results indicate that the MRF based methods, though slightly more complicated to program and only marginally slower than ICA de-noising, generally perform better than both ICA and Wiener filtering. The article ends by discussing future areas of research in de-noising remotely sensed images.