Iterative filtering and smoothing of measurements possessing poisson noise

2015 ◽  
Vol 51 (3) ◽  
pp. 2205-2011 ◽  
Author(s):  
G. A. Einicke
Keyword(s):  
Poisson Noise ◽  
Iterative Filtering

Detection of a Pairwise-Correlated Stream of Signals Against a Poisson Noise Background

1999 ◽  
Vol 53 (7-8) ◽  
pp. 69-78
Author(s):  
A. I. Strelkov ◽  
O. M. Stadnyk ◽  
S. I. Kalmykov ◽  
A. P. Lytyuga
Keyword(s):  
Poisson Noise ◽  
Noise Background

Algorithm for iterative filtering fast image inpainting

2013 ◽  
Vol 26 (3) ◽  
pp. 248-254
Author(s):  
Chun Liu ◽  
Zhiwei Kang ◽  
Yigang He
Keyword(s):  
Image Inpainting ◽  
Iterative Filtering

scholarly journals Directional TGV-Based Image Restoration under Poisson Noise

2021 ◽  
Vol 7 (6) ◽  
pp. 99
Author(s):  
Daniela di Serafino ◽  
Germana Landi ◽  
Marco Viola
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Data Fitting ◽  
Poisson Noise ◽  
Glass Fibres ◽  
Leibler Divergence ◽  
Generalized Variation ◽  
Low Computational Cost

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

scholarly journals Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Omar Lopez-Rincon ◽  
Oleg Starostenko ◽  
Vicente Alarcon-Aquino ◽  
Juan C. Galan-Hernandez
Keyword(s):  
Recognition Rate ◽  
Qr Code ◽  
Large Object ◽  
Position Detection ◽  
Object Based ◽  
Geometrical Features ◽  
Particular Solution ◽  
Variable Illumination ◽  
Low Performance ◽  
Iterative Filtering

Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%–100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.

On the phase detrending to disentangle refraction and diffraction on GNSS signals: a case study over Antarctica

2021 ◽  
Author(s):  
Luca Spogli ◽  
Hossein Ghobadi ◽  
Antonio Cicone ◽  
Lucilla Alfonsi ◽  
Claudio Cesaroni ◽  
...  
Keyword(s):  
Single Case ◽  
Cutoff Frequency ◽  
Satellite System ◽  
Fine Tuning ◽  
Scintillation Index ◽  
Ionospheric Scintillation ◽  
Iterative Filtering ◽  
Global Navigation Satellite ◽  
Value Changes

<p>We investigate the reliability of the phase scintillation index determined by receiving Global Navigation Satellite System (GNSS) signals at ground in the high-latitudes. To the scope, we report about the capabilities of recently introduced detrending scheme based on the signal decomposition provided by the Fast Iterative Filtering (FIF) technique. This detrending scheme enables a fine tuning of the cutoff frequency for phase detrending used in the phase scintillation index definition, aimed at disentangling diffraction and refraction effects. On a single case study based on GPS and Galileo data taken by a GNSS Ionospheric Scintillation Monitor Receiver (ISMR) in Concordia Station (Antarctica), we show how the FIF-based detrending allows deriving adaptive cutoff frequencies, whose value changes minute-by-minute. They are found to range between 0.4 Hz and 1.2 Hz. This allows better accounting for diffractive effects in phase scintillation index calculation and also showing the limitations on the use of such index, being still widely used in the community, both to characterize the features of ionospheric irregularities and to adopt mitigation solutions.</p>

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