In this paper, I apply the migration deconvolution to synthetic seismic images that have been distorted by cable feathering, migration velocity errors and recording geometry variations. The results show that migration deconvolution is able to suppress the velocity and geometry errors and can noticeably improve the quality of the migration image for both 2-D poststack and 3-D prestack migration....

where d(t) is the recorded signal and n(t) is the additive sensor noise signal. Some methods (Champagnat et al., 1996; Lavielle, 1993) used in Bayesian formulation the prior hypothesis that the reflectivity signal r(t) is a Bernouilli-Gaussian process. The first step is a detection of the reflectors and it follows by a magnitude estimation. The high noise level on recordings limits the performa...

We propose an efficient iterative curvelet-regularized deconvolution algorithm that exploits continuity along reflectors in seismic images. Curvelets are a new multiscale transform that provides sparse representations for images (such as seismic images) that comprise smooth objects separated by piece-wise smooth discontinuities. Our technique combines conjugate gradient-based convolution operat...

Deconvolution is a computationally intensive digital signal processing (DSP) function widely used in applications such as imaging, wireless communication, and seismology. In this paper deconvolution of two finite length sequences (NXM), is implemented using direct method to reduce deconvolution processing time. Vedic multiplier is used to achieve high speed. Urdhava Triyakbhyam algorithm of anc...

Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the convolution operator inversion. The Curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). Our iterative Curvelet-regularized deconvolution algorithm combine...

We propose a multi-channel speech dereverberation approach based on cross-channel cancellation and spectrogram decomposition. The reverberation is modeled as a convolution operation in the spectrogram magnitude domain. Using the Itakura divergence (I-divergence), we decompose reverberant spectrogram into clean spectrogram convolved with a deconvolution filter. The speech spectrogram is constrai...

In ultrasonic nondestructive evaluation (NDE) of materials, pulse echo measurements are masked by the characteristics of the measuring instruments, the propagation paths taken by the ultrasonic pulses, and are corrupted by additive noise. Deconvolution operation seeks to undo these masking effects and extract the defect impulse response which is essential for identification. In this contributio...

INTRODUCTION: In dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI), in order to estimate the Cerebral Blood Flow (CBF), a deconvolution operation between the arterial input function (AIF) and the voxel concentration of contrast agent (C(t)) must be performed to obtain the residue function (R(t)) [1]. Here, we propose a kernel based deconvolution approach that tackles the prob...

Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the convolution operator inversion. The Curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). Our iterative Curvelet-regularized deconvolution algorithm combine...

We present an iterative deconvolution algorithm that minimizes a functional with a non-quadratic waveletdomain regularization term. Our approach is to introduce subband-dependent parameters into the bound optimization framework of Daubechies et al.; it is sufficiently general to cover arbitrary choices of wavelet bases (non-orthonormal or redundant). The resulting procedure alternates between t...