Abstract: In seismic data processing, attenuation of random noise
is the basic step to improve quality of data for further application
of seismic data in exploration and development in different gas
and oil industries. The signal-to-noise ratio of the data also highly
determines quality of seismic data. This factor affects the reliability
as well as the accuracy of seismic signal during interpretation
for different purposes in different companies. To use seismic data
for further application and interpretation, we need to improve the
signal-to-noise ration while attenuating random noise effectively.
To improve the signal-to-noise ration and attenuating seismic
random noise by preserving important features and information
about seismic signals, we introduce the concept of anisotropic
total fractional order denoising algorithm. The anisotropic total
fractional order variation model defined in fractional order bounded
variation is proposed as a regularization in seismic denoising. The
split Bregman algorithm is employed to solve the minimization
problem of the anisotropic total fractional order variation model
and the corresponding denoising algorithm for the proposed method
is derived. We test the effectiveness of theproposed method for
synthetic and real seismic data sets and the denoised result is
compared with F-X deconvolution and non-local means denoising
algorithm.
Abstract: Diffuse Optical Tomography (DOT) is a non-invasive imaging modality used in clinical diagnosis for earlier detection of carcinoma cells in brain tissue. It is a form of optical tomography which produces gives the reconstructed image of a human soft tissue with by using near-infra-red light. It comprises of two steps called forward model and inverse model. The forward model provides the light propagation in a biological medium. The inverse model uses the scattered light to collect the optical parameters of human tissue. DOT suffers from severe ill-posedness due to its incomplete measurement data. So the accurate analysis of this modality is very complicated. To overcome this problem, optical properties of the soft tissue such as absorption coefficient, scattering coefficient, optical flux are processed by the standard regularization technique called Levenberg - Marquardt regularization. The reconstruction algorithms such as Split Bregman and Gradient projection for sparse reconstruction (GPSR) methods are used to reconstruct the image of a human soft tissue for tumour detection. Among these algorithms, Split Bregman method provides better performance than GPSR algorithm. The parameters such as signal to noise ratio (SNR), contrast to noise ratio (CNR), relative error (RE) and CPU time for reconstructing images are analyzed to get a better performance.