Fundamentals of digital image processing
Fundamentals of digital image processing
Ten lectures on wavelets
Complex Daubechies wavelets: properties and statistical image modelling
Signal Processing
A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orthogonal complex filter banks and wavelets: some properties anddesign
IEEE Transactions on Signal Processing
A new framework for complex wavelet transforms
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Multiscale MAP filtering of SAR images
IEEE Transactions on Image Processing
Speckle reducing anisotropic diffusion
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A steerable complex wavelet construction and its application to image denoising
IEEE Transactions on Image Processing
Wavelet transform domain filters: a spatially selective noise filtration technique
IEEE Transactions on Image Processing
Multilevel threshold based image denoising in curvelet domain
Journal of Computer Science and Technology
An envelope signal based deconvolution algorithm for ultrasound imaging
Signal Processing
VLSI-DSP based real time solution of DSC-SRI for an ultrasound system
Microprocessors & Microsystems
Fast reduction of speckle noise in real ultrasound images
Signal Processing
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Harmonic analysis filtering techniques for forced and decaying homogeneous isotropic turbulence
Computers & Mathematics with Applications
Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images
Digital Signal Processing
Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion
Multidimensional Systems and Signal Processing
Hi-index | 0.08 |
The paper presents a novel despeckling method, based on Daubechies complex wavelet transform, for medical ultrasound images. Daubechies complex wavelet transform is used due to its approximate shift invariance property and extra information in imaginary plane of complex wavelet domain when compared to real wavelet domain. A wavelet shrinkage factor has been derived to estimate the noise-free wavelet coefficients. The proposed method firstly detects strong edges using imaginary component of complex scaling coefficients and then applies shrinkage on magnitude of complex wavelet coefficients in the wavelet domain at non-edge points. The proposed shrinkage depends on the statistical parameters of complex wavelet coefficients of noisy image which makes it adaptive in nature. Effectiveness of the proposed method is compared on the basis of signal to mean square error (SMSE) and signal to noise ratio (SNR). The experimental results demonstrate that the proposed method outperforms other conventional despeckling methods as well as wavelet based log transformed and non-log transformed methods on test images. Application of the proposed method on real diagnostic ultrasound images has shown a clear improvement over other methods.