Patch-based adaptive temporal filter and residual evaluation
Weiying Zhao Paul Riot Charles-Alban Deledalle Henri Maître Jean-Marie Nicolas Florence Tupin
In coherent imaging systems, speckle is a signal-dependent noise that
visually strongly degrades images' appearance. A huge amount of SAR data has
been acquired from different sensors with different wavelengths, resolutions,
incidences and polarizations. We extend the nonlocal filtering strategy to the
temporal domain and propose a patch-based adaptive temporal filter (PATF) to
take advantage of well-registered multi-temporal SAR images. A patch-based
generalised likelihood ratio test is processed to suppress the changed object
effects on the multitemporal denoising results. Then, the similarities are
transformed into corresponding weights with an exponential function. The
denoised value is calculated with a temporal weighted average. Spatial adaptive
denoising methods can improve the patch-based weighted temporal average image
when the time series is limited. The spatial adaptive denoising step is
optional when the time series is large enough. Without reference image, we
propose using a patch-based auto-covariance residual evaluation method to
examine the ratio image between the noisy and denoised images and look for
possible remaining structural contents. It can process automatically and does
not rely on a supervised selection of homogeneous regions. It also provides a
global score for the whole image. Numerous results demonstrate the
effectiveness of the proposed time series denoising method and the usefulness
of the residual evaluation method.