3 research outputs found
Phase asymmetry guided adaptive fractional-order total variation and diffusion for feature-preserving ultrasound despeckling
It is essential for ultrasound despeckling to remove speckle noise while
simultaneously preserving edge features for accurate diagnosis and analysis in
many applications. To preserve real edges such as ramp edges and low contrast
edges, we first detect edges using a phase-based measure called phase asymmetry
(PAS), which can distinguish small differences in transition border regions and
varies from to , taking in ideal smooth regions and taking at
ideal step edges. We further propose three strategies to properly preserve
edges. First, in observing that fractional-order anisotropic diffusion (FAD)
filter has good performance in smooth regions while the fractional-order TV
(FTV) filter performs better at edges, we leverage the PAS metric to keep a
balance between FAD filter and FTV filter for achieving the best performance of
preserving ramp edges. Second, considering that the FAD filter fails to protect
low contrast edges by solely integrating gradient information into the
diffusion coefficient, we integrate the PAS metric into the diffusion
coefficient to properly preserve low contrast edges. Finally, different from
fixed fractional order diffusion filters neglecting the differences between
smooth regions and transition border regions, an adaptive fractional order is
implemented based on the PAS metric to enhance edges. The experimental results
show that our method outperforms other state-of-the-art ultrasound despeckling
filters in both speckle reduction and feature preservation
Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder
A fractional-based compressed auto-encoder architecture has been introduced
to solve the problem of denoising electroencephalogram (EEG) signals. The
architecture makes use of fractional calculus to calculate the gradients during
the backpropagation process, as a result of which a new hyper-parameter in the
form of fractional order () has been introduced which can be tuned to
get the best denoising performance. Additionally, to avoid substantial use of
memory resources, the model makes use of orthogonal features in the form of
Tchebichef moments as input. The orthogonal features have been used in
achieving compression at the input stage. Considering the growing use of low
energy devices, compression of neural networks becomes imperative. Here, the
auto-encoder's weights are compressed using the randomized singular value
decomposition (RSVD) algorithm during training while evaluation is performed
using various compression ratios. The experimental results show that the
proposed fractionally compressed architecture provides improved denoising
results on the standard datasets when compared with the existing methods.Comment: 9 pages, 8 figures, 26 reference
Expressway visibility estimation based on image entropy and piecewise stationary time series analysis
Vision-based methods for visibility estimation can play a critical role in
reducing traffic accidents caused by fog and haze. To overcome the
disadvantages of current visibility estimation methods, we present a novel
data-driven approach based on Gaussian image entropy and piecewise stationary
time series analysis (SPEV). This is the first time that Gaussian image entropy
is used for estimating atmospheric visibility. To lessen the impact of
landscape and sunshine illuminance on visibility estimation, we used region of
interest (ROI) analysis and took into account relative ratios of image entropy,
to improve estimation accuracy. We assume fog and haze cause blurred images and
that fog and haze can be considered as a piecewise stationary signal. We used
piecewise stationary time series analysis to construct the piecewise causal
relationship between image entropy and visibility. To obtain a real-world
visibility measure during fog and haze, a subjective assessment was established
through a study with 36 subjects who performed visibility observations.
Finally, a total of two million videos were used for training the SPEV model
and validate its effectiveness. The videos were collected from the constantly
foggy and hazy Tongqi expressway in Jiangsu, China. The contrast model of
visibility estimation was used for algorithm performance comparison, and the
validation results of the SPEV model were encouraging as 99.14% of the relative
errors were less than 10%