95 research outputs found
On the effect of image denoising on galaxy shape measurements
Weak gravitational lensing is a very sensitive way of measuring cosmological
parameters, including dark energy, and of testing current theories of
gravitation. In practice, this requires exquisite measurement of the shapes of
billions of galaxies over large areas of the sky, as may be obtained with the
EUCLID and WFIRST satellites. For a given survey depth, applying image
denoising to the data both improves the accuracy of the shape measurements and
increases the number density of galaxies with a measurable shape. We perform
simple tests of three different denoising techniques, using synthetic data. We
propose a new and simple denoising method, based on wavelet decomposition of
the data and a Wiener filtering of the resulting wavelet coefficients. When
applied to the GREAT08 challenge dataset, this technique allows us to improve
the quality factor of the measurement (Q; GREAT08 definition), by up to a
factor of two. We demonstrate that the typical pixel size of the EUCLID optical
channel will allow us to use image denoising.Comment: Accepted for publication in A&A. 8 pages, 5 figure
Classification of human parasitic worm using microscopic image processing technique
Human parasitic infection causes diseases to people whether this infection will be inside the body called endoparasites, or outside of the body called ectoparasites. Human intestinal parasite worms infected by air, food, and water are the causes of major diseases and health problems. So in this study, a technique to identify two types of parasites in human fecal, that is, the eggs of the worms is proposed. In this strategy, digital image processing methods such as noise reduction, contrast enhancement, and other morphological process are applied to extract the eggs images based on their features. The technique suggested in this study enables us to classify two different parasite eggs from their microscopic images which are roundworms (Ascaris lumbricoides ova, ALO) and whipworms (Trichuris trichiura ova, TTO). This proposed recognition method includes three stages. The first stage is a pre-processing sub-system, which is used to obtain unique features after performing noise reduction, contrast enhancement, edge enhancement, and detection. The next stage is an extraction mechanism which is based on five features of the three characteristics (shape, shell smoothness, and size. The final stage, the Filtration with Determinations Thresholds System (F-DTS) classifier is used to recognize the process using the ranges of feature values as a database to identify and classify the two types of parasites. The overall success rates are 93% and 94% in Ascaris lumbricoides and Trichuris trichiura, respectively
Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing with Prior Information
Commonly employed reconstruction algorithms in compressed sensing (CS) use
the norm as the metric for the residual error. However, it is well-known
that least squares (LS) based estimators are highly sensitive to outliers
present in the measurement vector leading to a poor performance when the noise
no longer follows the Gaussian assumption but, instead, is better characterized
by heavier-than-Gaussian tailed distributions. In this paper, we propose a
robust iterative hard Thresholding (IHT) algorithm for reconstructing sparse
signals in the presence of impulsive noise. To address this problem, we use a
Lorentzian cost function instead of the cost function employed by the
traditional IHT algorithm. We also modify the algorithm to incorporate prior
signal information in the recovery process. Specifically, we study the case of
CS with partially known support. The proposed algorithm is a fast method with
computational load comparable to the LS based IHT, whilst having the advantage
of robustness against heavy-tailed impulsive noise. Sufficient conditions for
stability are studied and a reconstruction error bound is derived. We also
derive sufficient conditions for stable sparse signal recovery with partially
known support. Theoretical analysis shows that including prior support
information relaxes the conditions for successful reconstruction. Simulation
results demonstrate that the Lorentzian-based IHT algorithm significantly
outperform commonly employed sparse reconstruction techniques in impulsive
environments, while providing comparable performance in less demanding,
light-tailed environments. Numerical results also demonstrate that the
partially known support inclusion improves the performance of the proposed
algorithm, thereby requiring fewer samples to yield an approximate
reconstruction.Comment: 28 pages, 9 figures, accepted in IEEE Transactions on Signal
Processin
An efficient, approximate path-following algorithm for elastic net based nonlinear spike enhancement
Unwanted spike noise in a digital signal is a common problem in digital filtering. However, sometimes the spikes are wanted and other, superimposed, signals are unwanted, and linear, time invariant (LTI) filtering is ineffective because the spikes are wideband - overlapping with independent noise in the frequency domain. So, no LTI filter can separate them, necessitating nonlinear filtering. However, there are applications in which the noise includes drift or smooth signals for which LTI filters are ideal. We describe a nonlinear filter formulated as the solution to an elastic net regularization problem, which attenuates band-limited signals and independent noise, while enhancing superimposed spikes. Making use of known analytic solutions a novel, approximate path-following algorithm is given that provides a good, filtered output with reduced computational effort by comparison to standard convex optimization methods. Accurate performance is shown on real, noisy electrophysiological recordings of neural spikes
Estimation from quantized Gaussian measurements: when and how to use dither
Subtractive dither is a powerful method for removing the signal dependence of quantization noise for coarsely quantized signals. However, estimation from dithered measurements often naively applies the sample mean or midrange, even when the total noise is not well described with a Gaussian or uniform distribution. We show that the generalized Gaussian distribution approximately describes subtractively dithered, quantized samples of a Gaussian signal. Furthermore, a generalized Gaussian fit leads to simple estimators based on order statistics that match the performance of more complicated maximum likelihood estimators requiring iterative solvers. The order statistics-based estimators outperform both the sample mean and midrange for nontrivial sums of Gaussian and uniform noise. Additional analysis of the generalized Gaussian approximation yields rules of thumb for determining when and how to apply dither to quantized measurements. Specifically, we find subtractive dither to be beneficial when the ratio between the Gaussian standard deviation and quantization interval length is roughly less than one-third. When that ratio is also greater than 0.822/K^0.930 for the number of measurements K > 20, estimators we present are more efficient than the midrange.https://arxiv.org/abs/1811.06856Accepted manuscrip
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