scholarly journals Spatial-Frequency domain nonlocal total variation for image denoising

2020 ◽  
Vol 14 (6) ◽  
pp. 1157-1184
Author(s):  
Haijuan Hu ◽  
◽  
Jacques Froment ◽  
Baoyan Wang ◽  
Xiequan Fan ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Frequency Domain ◽  
Image Denoising ◽  
Total Variation ◽  
Nonlocal Total Variation ◽  
Spatial Frequency Domain

Design of Digital Filters for Si Wafer Surface Profile Measurement - Denoising by Total Variation

2012 ◽  
Vol 516 ◽  
pp. 332-336
Author(s):  
Hirotaka Ojima ◽  
Kazutaka Nonomura ◽  
Li Bo Zhou ◽  
Jun Shimizu ◽  
Teppei Onuki
Keyword(s):  
High Resolution ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Total Variation ◽  
Digital Filters ◽  
Measured Data ◽  
Design Rule ◽  
Profile Measurement ◽  
Amplitude Component ◽  
Spatial Frequency Domain

In the semiconductor industry, high resolution and high accuracy measurement is needed for the geometric evaluation of Si wafers. The flatness parameters are important to evaluate the wafer profile and are required to be the same level as the design rule of IC, and the tolerance for flatness is very tight. According to SEMI (Semiconductor Equipment and Materials International) standards, the required wafer flatness will be 22 nanometres by the year 2016. However, to obtain a higher resolution for sensors, the uncertainty becomes very large compared to the resolution and influences the measured data when the noise is increased. High resolution instruments always incorporate a certain degree of noise. In the presence of noise, form parameters are normally biased. Correction and compensation need a large population of measurements to analytically estimate both bias and uncertainty. The estimation is still far from perfect because of the nature of noise. Another approach is to extract a true profile by filtering noise from the measured data. For the purpose of noise reduction, low-pass filters by Gaussian smoothing and Fourier transform are often used. The noise is normally considered to be a component of small deviation (amplitude) with high frequency which also takes a normal distribution around zero. However these conventional filters can remove the noise in the spatial frequency domain only. So, it is essential to design a filter capable of removing the noise both in the spatial frequency domain and the amplitude component. Thus, we have designed and developed new type of digital filter for denoising. We introduce two new digital filters. One is wavelet transform capable of denoising in the spatial frequency domain and amplitude component, and the other is total variation that can be applied to discontinuous signals without introducing artificial Gibbs Effects.

scholarly journals Preclinical evaluation of spatial frequency domain-enabled wide-field quantitative imaging for enhanced glioma resection

2017 ◽  
Vol 22 (7) ◽  
pp. 076007 ◽  
Author(s):  
Mira Sibai ◽  
Carl Fisher ◽  
Israel Veilleux ◽  
Jonathan T. Elliott ◽  
Frederic Leblond ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Frequency Domain ◽  
Quantitative Imaging ◽  
Wide Field ◽  
Preclinical Evaluation ◽  
Glioma Resection ◽  
Spatial Frequency Domain

Spatial frequency domain imaging with a bucket detector

2021 ◽  
Author(s):  
Armin J. M. Lenz ◽  
Pere Clemente ◽  
Vicent Climent ◽  
Jesús Lancis ◽  
Enrique Tajahuerce
Keyword(s):  
Spatial Frequency ◽  
Frequency Domain ◽  
Spatial Frequency Domain Imaging ◽  
Spatial Frequency Domain

Modulated imaging: quantitative analysis and tomography of turbid media in the spatial-frequency domain

2005 ◽  
Vol 30 (11) ◽  
pp. 1354 ◽  
Author(s):  
David J. Cuccia ◽  
Frederic Bevilacqua ◽  
Anthony J. Durkin ◽  
Bruce J. Tromberg
Keyword(s):  
Quantitative Analysis ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Turbid Media ◽  
Modulated Imaging ◽  
Spatial Frequency Domain
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