Neural shrinkage for wavelet-based SAR despeckling
This is an incremental improvement for SAR image processing, addressing the limitation of conventional wavelet shrinkage methods not being time-scale adaptive.
The paper tackled speckle reduction in Synthetic Aperture Radar (SAR) images by introducing a new Neural Shrinkage (NS) method with a novel shrinkage architecture, and the results showed it outperformed standard filters, standard wavelet shrinkage, and previous NS methods.
The wavelet shrinkage denoising approach is able to maintain local regularity of a signal while suppressing noise. However, the conventional wavelet shrinkage based methods are not time-scale adaptive to track the local time-scale variation. In this paper, a new type of Neural Shrinkage (NS) is presented with a new class of shrinkage architecture for speckle reduction in Synthetic Aperture Radar (SAR) images. The numerical results indicate that the new method outperforms the standard filters, the standard wavelet shrinkage despeckling method, and previous NS.