Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Speckle reducing anisotropic diffusion
IEEE Transactions on Image Processing
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images
IEEE Transactions on Image Processing
Thresholding neural network for adaptive noise reduction
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper represents a tow-step approach to improve de-speckling in SAR images. Firstly, Smoothing of the coefficients of the highest wavelet sub-bands is applied on decomposed wavelet coefficients. A Gaussian low pass filter using a trous algorithm has been used to decompose the image. Then, the learning of a Kohonen self organizing map (SOM) is performed directly on the denoised image to take out the blur. All traditional speckle reduction approaches cause artificial structures, blurred and smoothed image, so intelligent de-blurring technique captured these problems. Quantitative and qualitative comparisons of the results obtained by the new method with the results achieved from the other speckle noise reduction techniques demonstrate its higher performance for speckle reduction in SAR images.