Removal of partial Fourier‐induced Gibbs (RPG) ringing artifacts in MRI

2021 ◽  
Author(s):  
Hong‐Hsi Lee ◽  
Dmitry S. Novikov ◽  
Els Fieremans
Keyword(s):  
Ringing Artifacts

scholarly journals Blur-readable two-dimensional barcode based on blur-invariant shape and geometric features

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199958
Author(s):  
Shundao Xie ◽  
Hong-Zhou Tan
Keyword(s):  
Circular Disk ◽  
Geometric Features ◽  
Two Dimensional ◽  
Common Solution ◽  
Ringing Artifacts ◽  
2D Barcode ◽  
Defocus Blur ◽  
Blurred Image ◽  
Decoding Accuracy ◽  
Invariant Shape

In recent years, the application of two-dimensional (2D) barcode is more and more extensive and has been used as landmarks for robots to detect and peruse the information. However, it is hard to obtain a sharp 2D barcode image because of the moving robot, and the common solution is to deblur the blurry image before decoding the barcode. Image deblurring is an ill-posed problem, where ringing artifacts are commonly presented in the deblurred image, which causes the increase of decoding time and the limited improvement of decoding accuracy. In this article, a novel approach is proposed using blur-invariant shape and geometric features to make a blur-readable (BR) 2D barcode, which can be directly decoded even when seriously blurred. The finder patterns of BR code consist of two concentric rings and five disjoint disks, whose centroids form two triangles. The outer edges of the concentric rings can be regarded as blur-invariant shapes, which enable BR code to be quickly located even in a blurred image. The inner angles of the triangle are of blur-invariant geometric features, which can be used to store the format information of BR code. When suffering from severe defocus blur, the BR code can not only reduce the decoding time by skipping the deblurring process but also improve the decoding accuracy. With the defocus blur described by circular disk point-spread function, simulation results verify the performance of blur-invariant shape and the performance of BR code under blurred image situation.

An integrated broadband preprocessing method for towed-streamer seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. V201-V221 ◽  
Author(s):  
Mehdi Aharchaou ◽  
Erik Neumann
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Pressure Data ◽  
Amplitude Variation ◽  
Field Source ◽  
Amplitude Variation With Offset ◽  
Ringing Artifacts ◽  
Joint Application ◽  
Amplitude Compensation ◽  
Unified Method

Broadband preprocessing has become widely used for marine towed-streamer seismic data. In the standard workflow, far-field source designature, receiver and source-side deghosting, and redatuming to mean sea level are applied in sequence, with amplitude compensation for background [Formula: see text] delayed until the imaging or postmigration stages. Thus, each step is likely to generate its own artifacts, quality checking can be time-consuming, and broadband data are only obtained late in this chained workflow. We have developed a unified method for broadband preprocessing — called integrated broadband preprocessing (IBP) — which enables the joint application of all the above listed steps early in the processing sequence. The amplitude, phase, and amplitude-variation-with-offset fidelity of IBP are demonstrated on pressure data from the shallow, deep, and slanted streamers. The integration allows greater sparsity to emerge in the representation of seismic data, conferring clear benefits over the sequential application. Moreover, time sparsity, full dimensionality, and early amplitude [Formula: see text] compensation all have an impact on broadband data quality, in terms of reduced ringing artifacts, improved wavelet integrity at large crossline angles, and fewer residual high-frequency multiples.

scholarly journals Reduction of Ringing Artifacts in the Arterial Phase of Gadoxetic Acid-enhanced Dynamic MR Imaging

2012 ◽  
Vol 11 (2) ◽  
pp. 91-97 ◽  
Author(s):  
Akihiro TANIMOTO ◽  
Nobuya HIGUCHI ◽  
Akihisa UENO
Keyword(s):  
Mr Imaging ◽  
Gadoxetic Acid ◽  
Arterial Phase ◽  
Dynamic Mr Imaging ◽  
Ringing Artifacts ◽  
Dynamic Mr

Locating Edges and Removing Ringing Artifacts in JPEG Images by Frequency-Domain Analysis

2007 ◽  
Vol 16 (5) ◽  
pp. 1470-1474 ◽  
Author(s):  
Irina Popovici ◽  
Wm. Douglas Withers
Keyword(s):  
Frequency Domain ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Jpeg Images ◽  
Ringing Artifacts

Image quality improvement of random phase-free holograms by addressing the cause of ringing artifacts

2019 ◽  
Vol 58 (9) ◽  
pp. 2146 ◽  
Author(s):  
Yuki Nagahama ◽  
Tomoyoshi Shimobaba ◽  
Takashi Kakue ◽  
Yasuhiro Takaki ◽  
Tomoyoshi Ito
Keyword(s):  
Quality Improvement ◽  
Image Quality ◽  
Random Phase ◽  
Ringing Artifacts ◽  
Image Quality Improvement

Filter banks for less ringing artifacts

2002 ◽  
Vol 85 (6) ◽  
pp. 1-10
Author(s):  
Wataru Asano ◽  
Tomoaki Takeuchi ◽  
Masaaki Ikehara
Keyword(s):  
Filter Banks ◽  
Ringing Artifacts

S/N Enhancement of Raman Spectra Using an Interactive Filter

1993 ◽  
Vol 47 (11) ◽  
pp. 1943-1948 ◽  
Author(s):  
Charles K. Mann ◽  
Thomas J. Vickers
Keyword(s):  
Least Squares ◽  
Raman Spectra ◽  
Minor Component ◽  
Limited Range ◽  
Spectral Features ◽  
Ringing Artifacts ◽  
Sharp Cutoff

The use of an interactive filter for smoothing Raman spectra is described. It is applied by fitting a polynomial to a limited range of data in a spectrum using the least-squares criterion. It provides a variable bandpass with very sharp cutoff without generating ringing artifacts. Its use is demonstrated in tailoring of the filter bandpass to the requirements of individual spectral features, in detection of weak peaks in a noisy spectrum, and in detection of minor component signals when these are superimposed on major component signals.

scholarly journals Image Fusion Based Multimodal Biometric Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 3613-3617
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
The Body ◽  
Biometric Authentication ◽  
Biometric Data ◽  
Ringing Artifacts ◽  
Fused Image ◽  
Focus Image ◽  
Multimodal Biometric Recognition ◽  
Authentic Data

Biometric Authentication is a security process that replays on the unique biological characteristics of an individual. Biometric Authentication system compare a biometric data capture to stored, confirmed authentic data in a database. It is simply the process of verifying the identity using the measurements or other unique characteristics of the body, then logging us in a service, device and so on. It is an effective way to prove identity because it can’t be replicated. Multi focus Image fusion is a process of fusing two or more images to obtain a new one. Used to reduce the problems like blocking, ringing artifacts occurs because of DCT. The low frequency sub-band coefficients are fused by selecting coefficient having maximum spatial frequency. The goal is classifying the images to classes of authorized and unauthorized using multi class SVM. The fingerprint image and iris image are fused together using SWT, the features are extracted from the fused image and labelled using GLCM algorithm. The testing image is then compared with trained samples and classified as authorized or unauthorized by using FFNN.

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