Denoising based on wavelets and deblurring via self-organizing map for Synthetic Aperture Radar images
This addresses image quality improvement for synthetic aperture radar or multimedia applications, but it appears incremental as it combines existing techniques like wavelets and SOMs.
The paper tackles unsupervised image deblurring for low-resolution SAR or multimedia images with multiplicative or additive noise, proposing a two-step method combining wavelet-based denoising and SOM-based deblurring, with successful application to real SAR images and simulation results demonstrating effectiveness.
This work deals with unsupervised image deblurring. We present a new deblurring procedure on images provided by low-resolution synthetic aperture radar (SAR) or simply by multimedia in presence of multiplicative (speckle) or additive noise, respectively. The method we propose is defined as a two-step process. First, we use an original technique for noise reduction in wavelet domain. Then, the learning of a Kohonen self-organizing map (SOM) is performed directly on the denoised image to take out it the blur. This technique has been successfully applied to real SAR images, and the simulation results are presented to demonstrate the effectiveness of the proposed algorithms.