Abstract: In this paper we introduced new wavelet based algorithm for speckle reduction of synthetic aperture radar images, which uses combination of undecimated wavelet transformation, wiener filter (which is an adaptive filter) and mean filter. Further more instead of using existing thresholding techniques such as sure shrinkage, Bayesian shrinkage, universal thresholding, normal thresholding, visu thresholding, soft and hard thresholding, we use brute force thresholding, which iteratively run the whole algorithm for each possible candidate value of threshold and saves each result in array and finally selects the value for threshold that gives best possible results. That is why it is slow as compared to existing thresholding techniques but gives best results under the given algorithm for speckle reduction.
Abstract: This paper introduces the effective speckle reduction of
synthetic aperture radar (SAR) images using inner product spaces in
undecimated wavelet domain. There are two major areas in projection
onto span algorithm where improvement can be made. First is the use
of undecimated wavelet transformation instead of discrete wavelet
transformation. And second area is the use of smoothing filter namely
directional smoothing filter which is an additional step. Proposed
method does not need any noise estimation and thresholding
technique. More over proposed method gives good results on both
single polarimetric and fully polarimetric SAR images.
Abstract: Speckle noise affects all coherent imaging systems
including medical ultrasound. In medical images, noise suppression
is a particularly delicate and difficult task. A tradeoff between noise
reduction and the preservation of actual image features has to be made
in a way that enhances the diagnostically relevant image content.
Even though wavelets have been extensively used for denoising
speckle images, we have found that denoising using contourlets gives
much better performance in terms of SNR, PSNR, MSE, variance and
correlation coefficient. The objective of the paper is to determine the
number of levels of Laplacian pyramidal decomposition, the number
of directional decompositions to perform on each pyramidal level and
thresholding schemes which yields optimal despeckling of medical
ultrasound images, in particular. The proposed method consists of the
log transformed original ultrasound image being subjected to contourlet
transform, to obtain contourlet coefficients. The transformed
image is denoised by applying thresholding techniques on individual
band pass sub bands using a Bayes shrinkage rule. We quantify the
achieved performance improvement.
Abstract: This paper describes a novel projection algorithm, the Projection Onto Span Algorithm (POSA) for wavelet-based superresolution and removing speckle (in wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images. Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands; POSA is applied to the high subbands, and reconstruct a SAR image from the modified detail coefficients. Experimental results demonstrate that the new method compares favorably to several other despeckling methods on test SAR images.
Abstract: 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.