Kalman's shrinkage for wavelet-based despeckling of SAR images
This addresses speckle noise reduction in SAR images, which is important for remote sensing applications, but it appears incremental as it builds on existing wavelet-based methods.
The paper tackled despeckling in synthetic aperture radar (SAR) images by proposing a new probability density function for wavelet coefficients and deriving a Kalman filter from it, resulting in a method that compares favorably to other despeckling techniques on test images.
In this paper, a new probability density function (pdf) is proposed to model the statistics of wavelet coefficients, and a simple Kalman's filter is derived from the new pdf using Bayesian estimation theory. Specifically, we decompose the speckled image into wavelet subbands, we apply the Kalman's filter to the high subbands, and reconstruct a despeckled image from the modified detail coefficients. Experimental results demonstrate that our method compares favorably to several other despeckling methods on test synthetic aperture radar (SAR) images.