As an active remote sensing instrument, lidar provides a high spatial resolution vertical profile of aerosol optical properties. But the effective range and data reliability are often limited by various noises. Performing a proper denoising method will improve the quality of the signals obtained. The denoising method based on ensemble empirical mode decomposition (EEMD) is introduced, but the d...

A hybrid denoising method is presented as a combination of Empirical Mode Decomposition (EMD) and Higher Order Statistics (HOS). EMD, an adaptive data-driven method, is used for effective decomposition of a noisy signal into its functional components. Then Kurtosis and Bispectrum operate as Gaussianity estimators, supplemented by Bootstrap techniques, ensuring detection and removal of the signa...

The main challenge in digital image processing is to remove noise from the original image. This paper reviews the existing denoising algorithms and performs their comparative study. Different noise models including additive and multiplicative types are discussed in the paper.Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise p...

a r t i c l e i n f o a b s t r a c t The lightning current derivative data recorded at the CN Tower during the past 18 years contain different kinds of noise and needs to be denoised for accurately determining the lightning current waveform parameters. It is usually a challenging task to denoise transient signals having large bandwidth without altering their waveshapes or shrinking their ampli...

Estimating the images using decimated wavelet transform is very popular technique in different applications. In this paper a new thresholding function with combination of Smoothly Clipped Absolute Deviation (SCAD), Hard thresholding and soft thresholding functions are introduced for wavelet based denoising of images. The proposed technique is applied for denoising of noisy images contaminated w...

This paper introduces an image denoising algorithm based on generalized Srivastava-Owa fractional differential operator for removing Gaussian noise in digital images. The structures of n n× fractional masks are constructed by this algorithm. Experiments show that, the capability of the denoising algorithm by fractional differential-based approach appears efficient to smooth the Gaussian noisy i...

Image denoising is an important task in image processing. The interest in using a fractional mask window operator based on fractional calculus has grown for image denoising. This paper mainly introduces the concept of fractional calculus and proposes a new mathematical method in using fractional Alexander polynomials for image denoising. The structures of n n fractional mask windows on eight di...

Total variation denoising (TVD) is an approach for noise reduction developed so as to preserve sharp edges in the underlying signal. Unlike a conventional low-pass lter, TV denoising is de ned in terms of an optimization problem. This module describes an algorithm for TV denoising derived using the majorization-minimization (MM) approach, developed by Figueiredo et al. [ICIP 2006]. To keep it s...

Signal acquisition is a noisy business. In photographic images, there is noise within the light intensity signal (e.g., photon noise), and additional noise can arise within the sensor (e.g., thermal noise in a CMOS chip), as well as in subsequent processing (e.g., quantization). Image noise can be quite noticeable, as in images captured by inexpensive cameras built into cellular telephones, or ...

In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following “SOS” procedure: (i) Strengthen the signal by adding the previous denoised image to the degraded input image, (ii) Operate the denoising method on the strengthened image, and (iii) Subtract the previous denoised image from the restore...