@inproceedings {284, title = {Structure-preserving speckle reduction of SAR images using nonlocal means filters}, booktitle = {16th IEEE International Conference on Image Processing (ICIP)}, year = {2009}, month = {11/2009}, address = {Cairo, Egypt}, abstract = {

This paper proposes a structure-preserving speckle reduction (SPSR) algorithm for synthetic aperture radar (SAR) images by exploiting self-similarity of structural patterns based on nonlocal means filter. The SPSR algorithm is featured by discerning pixels of similar structural patterns, which is crucial for a despeckling process to avoid blurring image structure. To alleviate the impact of speckle noise to similarity measure, a two-stage filtering scheme is introduced into the SPSR algorithm. Filtering at the first stage aims at an accurate approximation of true structural similarity, followed by the filtering at the second stage to group pixels with similar neighborhood in a large area. Compared to the traditional Lee filter, enhanced Lee filter and the speckle reducing anisotropic diffusion (SRAD), evaluation results have shown that the SPSR algorithm substantially improves the despeckling performance especially on structure preservation and speckle reduction in homogeneous regions.

}, keywords = {enhanced Lee filter, filtering theory, Image Denoising, nonlocal means filters, radar imaging, SAR images, speckle reducing anisotropic diffusion, structure-preserving speckle reduction, synthetic aperture radar, synthetic aperture radar images, two-stage filtering scheme}, doi = {http://dx.doi.org/10.1109/ICIP.2009.5414502}, author = {X Yang and D A. Clausi} } @inproceedings {362, title = {SAR sea ice image segmentation based on edge-preserving watersheds}, booktitle = {4th Annual Canadian Conference on Computer and Robot Vision}, year = {2007}, month = {05/2007}, address = {Montreal, Quebec, Canada}, abstract = {

This paper presents a hybrid method for the segmentation of SAR sea ice images, which consists of an initial watershed segmentation followed by a region merging. Iterative bilateral filtering is used to reduce speckle noise and suppress irrelevant image details, which can significantly alleviate oversegmentation of watersheds. Since edges are well preserved by bilateral filtering, the watershed algorithm is capable of precisely locating object boundaries. Final segmentation is accomplished by applying an iterative region merging on the watershed regions by taking into account local boundary strengths and regional statistics. The efficiency of the proposed method has been demonstrated on the segmentation of SAR sea ice images. In comparison with traditional watershed algorithm, our method achieves better performance in identifying filament structures such as leads.

}, keywords = {edge-preserving watersheds, filtering theory, image segmentation, iterative bilateral filtering, iterative methods, local boundary strengths, radar imaging, region merging, regional statistics, SAR sea ice image segmentation, speckle noise, statistical analysis, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CRV.2007.58}, author = {X Yang and D A. Clausi} } @inproceedings {395, title = {A combined Bayesshrink wavelet-ridgelet technique for image denoising}, booktitle = {2006 IEEE International Conference on Multimedia and Expo}, year = {2006}, abstract = {

In this paper a combined Bayesshrink wavelet-ridgelet de-noising method is presented. In our previous work we have showed that Bayesshrink ridgelet performs better than Visushrink ridgelet and Visushrink wavelet. Although our Bayesshrink ridgelet technique performs somewhat poorer in comparison with Bayesshrink wavelet, based on SNR, visually it produces smoother results, especially for images with straight lines. In the proposed method Bayesshrink wavelet is combined with Bayesshrink ridgelet denoising method which performs better than each filter individually. The proposed combined denoising method gains the advantage of each filter in its specific domain, i.e., wavelet for natural and ridgelet for straight regions, and produces better and smoother results, both visually and in terms of SNR

}, keywords = {Bayes methods, Bayesshrink wavelet ridgelet technique, filtering theory, Image Denoising, image denoising method, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICME.2006.262931}, author = {N Nezamoddin Kachouie and P Fieguth} }