In optical metrology, state of the art algorithms for background and noise removal of fringe patterns are based on space-frequency analysis. In this Letter, an approach based on variational image decomposition is proposed to remove background and noise from a fringe pattern simultaneously. In the proposed method, a fringe image is directly decomposed into three components: a first one containing background, a second one fringes, and a third one noise, which are described in different function spaces and are solved by minimization of the functional. A simple technical process involved in the minimization algorithm improves the convergence performance. The proposed approach is verified with the simulated and experimental fringe patterns.
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http://dx.doi.org/10.1364/OL.38.000275 | DOI Listing |
Eur J Radiol Open
June 2025
Department of Radiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
Background: Deep learning (DL) accelerated controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-volumetric interpolated breath-hold examination (VIBE), provides high spatial resolution T1-weighted imaging of the upper abdomen. We aimed to investigate whether DL-CAIPIRINHA-VIBE can improve image quality, vessel conspicuity, and lesion detectability compared to a standard CAIPIRINHA-VIBE in renal imaging at 3 Tesla.
Methods: In this prospective study, 50 patients with 23 solid and 45 cystic renal lesions underwent MRI with clinical MR sequences, including standard CAIPIRINHA-VIBE and DL-CAIPIRINHA-VIBE sequences in the nephrographic phase at 3 Tesla.
Med Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFLang Speech
January 2025
Department of Educational Psychology, Leadership, & Counseling, Texas Tech University, USA.
Adapting one's speaking style is particularly crucial as children start interacting with diverse conversational partners in various communication contexts. The study investigated the capacity of preschool children aged 3-5 years ( = 28) to modify their speaking styles in response to background noise, referred to as noise-adapted speech, and when talking to an interlocutor who pretended to have hearing loss, referred to as clear speech. We examined how two modified speaking styles differed across the age range.
View Article and Find Full Text PDFJ Occup Med Toxicol
January 2025
School of Health Sciences, Department of Audiology, University of the Pacific, San Francisco, California, USA.
Background: Hazardous noise exposure is an important health concern in many workplaces and is one of the most common work-related injuries in the United States. Dental professionals are frequently exposed to high levels of occupational noise in their daily work environment. This noise is generated by various dental handpieces such as drills, suctions, and ultrasonic scalers.
View Article and Find Full Text PDFJ Neurodev Disord
January 2025
Graduate Neuroscience Program, University of California, Riverside, CA, USA.
Background: Fragile X syndrome (FXS) is a leading known genetic cause of intellectual disability and autism spectrum disorders (ASD)-associated behaviors. A consistent and debilitating phenotype of FXS is auditory hypersensitivity that may lead to delayed language and high anxiety. Consistent with findings in FXS human studies, the mouse model of FXS, the Fmr1 knock out (KO) mouse, shows auditory hypersensitivity and temporal processing deficits.
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