98 research outputs found
Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments
The localized nature of curvelet functions, together with their frequency and
dip characteristics, makes the curvelet transform an excellent choice for
processing seismic data. In this work, a denoising method is proposed based on
a combination of the curvelet transform and a whitening filter along with
procedure for noise variance estimation. The whitening filter is added to get
the best performance of the curvelet transform under coherent and incoherent
correlated noise cases, and furthermore, it simplifies the noise estimation
method and makes it easy to use the standard threshold methodology without
digging into the curvelet domain. The proposed method is tested on
pseudo-synthetic data by adding noise to real noise-less data set of the
Netherlands offshore F3 block and on the field data set from east Texas, USA,
containing ground roll noise. Our experimental results show that the proposed
algorithm can achieve the best results under all types of noises (incoherent or
uncorrelated or random, and coherent noise)
Scale Selective Extended Local Binary Pattern for Texture Classification
In this paper, we propose a new texture descriptor, scale selective extended
local binary pattern (SSELBP), to characterize texture images with scale
variations. We first utilize multi-scale extended local binary patterns (ELBP)
with rotation-invariant and uniform mappings to capture robust local micro- and
macro-features. Then, we build a scale space using Gaussian filters and
calculate the histogram of multi-scale ELBPs for the image at each scale.
Finally, we select the maximum values from the corresponding bins of
multi-scale ELBP histograms at different scales as scale-invariant features. A
comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows
that the proposed SSELBP has high accuracy comparable to state-of-the-art
texture descriptors on gray-scale-, rotation-, and scale-invariant texture
classification but uses only one-third of the feature dimension.Comment: IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 201
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