DM-Free Curvelet Based Denoising for Astronomical Single Pulse Detection

Jiang, Min; Cui, Bingyi; Yu, Yu-Feng; Cao, Zhicheng

doi:10.1109/access.2019.2933387

Cited by 4 publications

(7 citation statements)

References 31 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the input is cartesian arrays such as an image of the form

f ([],, t_{1}, t_{2})

, 0

\leq t_{1}, t_{2} \leq p

, where t 1 , t 2 , and p are real numbers then digital curvelet transforms using three parameters scale, rotation angles and translation parameter can be defined as 30 :

{cvt}^{D} ((), j, l, normalζ) = \sum_{0 \leq t_{1}, t_{2} \leq p} f ([],, t_{1}, t_{2}) \overset{ψ_{j, l, normalζ}^{D} ([],, t_{1}, t_{2})}{true} .

…”

Section: Methodsmentioning

confidence: 99%

“…Jean‐Luc et al 19 proposes curvelet‐based multiscale denoising using non‐local means and guided image filter. In the two‐dimensional (2D) domain ℝ2, all curvelets at scale 2‐ j are found by rotation and translations of mother curvelet ψ j with different parameters 30 . Curvelets are defined at scale 2‐ j , orientation θ l and position

x_{ζ}^{j, l}

in Equation ().

ψ_{j, l, normalζ} ((), x) = ψ_{j} ((), R_{{normalθ}_{l}} ((), x - x_{ζ}^{j, l})),

where position

x_{ζ}^{j, l} = R_{{normalθ}_{l}}^{- 1} ((),, ζ_{1} . 2^{- j}, ζ_{2} \cdot 2^{- \frac{j}{2}}),

R_{θ}

is rotation by θ radians and

R_{θ}^{- 1}

is its inverse.…”

Section: Methodsmentioning

confidence: 99%

“…In the two-dimensional (2D) domain ℝ2, all curvelets at scale 2-j are found by rotation and translations of mother curvelet ψ j with different parameters. 30 Curvelets are defined at scale 2-j, orientation θ l and position x j,l ζ in Equation (1).…”

Section: Denoising Using Curveletmentioning

confidence: 99%

See 2 more Smart Citations

Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

Choudhary

Singhai

Yadav

2021

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Effective detection and removal of the artifacts from dermoscopic images require the analysis of structures within lesions for accurate cancer detection. But the dermoscopic images acquired may be affected by image acquisition noise, variation in texture, color, and irrelevant structure such as hairs, air bubbles, and contour blurring around the lesion. Hence preprocessing is very essential for accurate diagnosis. The proposed algorithm includes the preprocessing, to remove acquisition noise using curvelet transform, illumination correction using contrast limited adaptive histogram equalization (CLAHE) algorithm for image enhancement, Frangi vesselness algorithm is utilized for detection and removal of hairs and vessels like structures present in enhanced image, fast marching inpainting method is applied for repairing the information of lesion from removed hair pixels. The performance of the proposed algorithm is analyzed based on commonly used parameters such as diagnostic accuracy (DA), sensitivity (SE), specificity (SP), and precision (P). The performance is evaluated on PH2 and International Symposium on Biomedical Imaging (ISBI) datasets than compared with existing methods. The proposed algorithm segments the skin lesion with the highest diagnostic accuracy of 81.49% and sensitivity of 93.88% for the PH2 dataset and 75.39% and 79.34% respectively for the ISBI dataset. Results are better than existing methods. The proposed algorithm with improved preprocessing for artifact detection and removal is highly accurate and able to retrieve the hair occluded information.

show abstract

“…When the input is cartesian arrays such as an image of the form

f ([],, t_{1}, t_{2})

, 0

\leq t_{1}, t_{2} \leq p

, where t 1 , t 2 , and p are real numbers then digital curvelet transforms using three parameters scale, rotation angles and translation parameter can be defined as 30 :

{cvt}^{D} ((), j, l, normalζ) = \sum_{0 \leq t_{1}, t_{2} \leq p} f ([],, t_{1}, t_{2}) \overset{ψ_{j, l, normalζ}^{D} ([],, t_{1}, t_{2})}{true} .

…”

Section: Methodsmentioning

confidence: 99%

x_{ζ}^{j, l}

in Equation ().

ψ_{j, l, normalζ} ((), x) = ψ_{j} ((), R_{{normalθ}_{l}} ((), x - x_{ζ}^{j, l})),

where position

x_{ζ}^{j, l} = R_{{normalθ}_{l}}^{- 1} ((),, ζ_{1} . 2^{- j}, ζ_{2} \cdot 2^{- \frac{j}{2}}),

R_{θ}

is rotation by θ radians and

R_{θ}^{- 1}

is its inverse.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

Choudhary

Singhai

Yadav

2021

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…The approach there uses time-frequency images of the data as input to a neural network classifier, which can detect the RFI. An alternative machine learning approach is a support vector machine, which is applied in [14] to recognize transients from features also extracted from a time-frequency representation. Reference [15] describes the application of two types of neural networks for RFI detection.…”

Section: Overview Of State-of-the-artmentioning

confidence: 99%

Assessment of Techniques for Detection of Transient Radio-Frequency Interference (RFI) Signals: A Case Study of a Transient in Radar Test Data

Durden,

Vilnrotter,

Shaffer

2023

Eng

View full text Add to dashboard Cite

The authors present a case study of the investigation of a transient signal that appeared in the testing of a radar receiver. The characteristics of the test conditions and data are first discussed. The authors then proceed to outline the methods for detecting and analyzing transients in the data. For this, they consider several methods based on modern signal processing and evaluate their utility. The initial method used for identifying transients is based on computer vision techniques, specifically, thresholding spectrograms into binary images, morphological processing, and object boundary extraction. The authors also consider deep learning methods and methods related to optimal statistical detection. For the latter approach, since the transient in this case was chirp-like, the method of maximum likelihood is used to estimate its parameters. Each approach is evaluated, followed by a discussion of how the results could be extended to analysis and detection of other types of transient radio-frequency interference (RFI). The authors find that computer vision, deep learning, and statistical detection methods are all useful. However, each is best used at different stages of the investigation when a transient appears in data. Computer vision is particularly useful when little is known about the transient, while traditional statistically optimal detection can be quite accurate once the structure of the transient is known and its parameters estimated.

show abstract

“…The latter have also been studied in a CMB framework by Vafaei Sadr et al ( 2017) and Hergt et al (2017), using the curvelet transform, while Laliberte et al (2018) use ridgelets to that end within N-body simulations. Gallagher et al (2011) apply both to solar astrophysics, and Jiang et al (2019) use curvelets for radio transient detection.…”

Section: Des Ridges and Curvelet Comparisonmentioning

confidence: 99%

Ridges in the Dark Energy Survey for cosmic trough identification

Moews,

Schmitz,

Lawler

et al. 2020

Preprint

View full text Add to dashboard Cite

Cosmic voids and their corresponding redshift-aggregated projections of mass densities, known as troughs, play an important role in our attempt to model the large-scale structure of the Universe. Understanding these structures leads to tests comparing the standard model with alternative cosmologies, constraints on the dark energy equation of state, and provides evidence to differentiate among gravitational theories. In this paper, we extend the subspace-constrained mean shift algorithm, a recently introduced method to estimate density ridges, and apply it to 2D weak-lensing mass density maps from the Dark Energy Survey Y1 data release to identify curvilinear filamentary structures. We compare the obtained ridges with previous approaches to extract trough structure in the same data, and apply curvelets as an alternative wavelet-based method to constrain densities. We then invoke the Wasserstein distance between noisy and noiseless simulations to validate the denoising capabilities of our method. Our results demonstrate the viability of ridge estimation as a precursor for denoising weak lensing quantities to recover the large-scale structure, paving the way for a more versatile and effective search for troughs.

show abstract

DM-Free Curvelet Based Denoising for Astronomical Single Pulse Detection

Cited by 4 publications

References 31 publications

Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

Assessment of Techniques for Detection of Transient Radio-Frequency Interference (RFI) Signals: A Case Study of a Transient in Radar Test Data

Ridges in the Dark Energy Survey for cosmic trough identification

Contact Info

Product

Resources

About