Abstract: One of the most important challenging factors in
medical images is nominated as noise. Image denoising refers to the
improvement of a digital medical image that has been infected by
Additive White Gaussian Noise (AWGN). The digital medical image
or video can be affected by different types of noises. They are
impulse noise, Poisson noise and AWGN. Computed tomography
(CT) images are subjects to low quality due to the noise. Quality of
CT images is dependent on absorbed dose to patients directly in such
a way that increase in absorbed radiation, consequently absorbed
dose to patients (ADP), enhances the CT images quality. In this
manner, noise reduction techniques on purpose of images quality
enhancement exposing no excess radiation to patients is one the
challenging problems for CT images processing. In this work, noise
reduction in CT images was performed using two different
directional 2 dimensional (2D) transformations; i.e., Curvelet and
Contourlet and Discrete Wavelet Transform (DWT) thresholding
methods of BayesShrink and AdaptShrink, compared to each other
and we proposed a new threshold in wavelet domain for not only
noise reduction but also edge retaining, consequently the proposed
method retains the modified coefficients significantly that result good
visual quality. Data evaluations were accomplished by using two
criterions; namely, peak signal to noise ratio (PSNR) and Structure
similarity (Ssim).
Abstract: This work proposes an accurate crosstalk noise estimation method in the presence of multiple RLC lines for the use in design automation tools. This method correctly models the loading effects of non switching aggressors and aggressor tree branches using resistive shielding effect and realistic exponential input waveforms. Noise peak and width expressions have been derived. The results obtained are at good agreement with SPICE results. Results show that average error for noise peak is 4.7% and for the width is 6.15% while allowing a very fast analysis.