Directional clutter removal of aerial digital images using X-ray wavelet transform and Markov random field

Aerial images are one of the primary data sources for underwater oceanographic studies. These images are often corrupted by clutter induced by surface water waves. Removal of the wave clutter from these images is an important preprocessing step for accurate assessment of information. In this paper, we introduce a novel technique combining the X-ray wavelet transform (XWT) with Markov random field (MRF) for directional noise removal. Surface water waves are classified according to their features into two types: ripple wave (long-wave) and spark wave (short-wave). We show in our numerical experiments that by performing XWT along the direction of wave propagation, the wave clutter can he successfully detected. To remove long-waves, resampling and subband filtering techniques are used. To remove short-waves, on the other hand, a spectral-spatial maximum exclusive mean (SMEM) filter is used in this study. Finally, because of the directional characteristic of the clutter, nonisotropic MRF is introduced into the post-processing step to refine the output. Experimental results show that one can remove both kinds of wave clutter with only small background distortion using the proposed hybrid algorithm.