Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?

2012 ◽  
Vol 68 (6) ◽  
pp. 1983-1993 ◽  
Author(s):  
Esben Plenge ◽  
Dirk H. J. Poot ◽  
Monique Bernsen ◽  
Gyula Kotek ◽  
Gavin Houston ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Trade Off ◽  
Noise Ratio

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Image Acquisition Using Non-Pixeled Amorphous Silicon Based Sensors

2001 ◽  
Vol 685 ◽  
Author(s):  
M. Fernandes ◽  
Yu. Vygranenko ◽  
J. Martins ◽  
M. Vieira
Keyword(s):  
Amorphous Silicon ◽  
Signal To Noise Ratio ◽  
Image Acquisition ◽  
Short Circuit ◽  
Acquisition Time ◽  
Large Area ◽  
Signal To Noise ◽  
Scanning Beam ◽  
Noise Ratio ◽  
Silicon Based

AbstractWe suggest to enhance the performance of image acquisition systems based on large area amorphous silicon based sensors by optimizing the readout parameters such as the intensity and cross-section of scanner beam, acquisition time and bias conditions. The main output device characteristics as image responsivity, signal to noise ratio and spatial resolution were analyzed in open circuit, short circuit and photodiode modes. The result show that the highest signal to noise ratio and best dark to bright ratio can be achieved in short circuit mode.It was shown that the sensor resolution is related to the basic device parameters and, in practice, limited by the acquisition time and scanning beam properties. The scanning beam spot size limits the resolution due to the overlapping of dark and illuminated zones leading to a blurring effect on the final image and a consequent degradation in the resolution.

scholarly journals Evaluation of sted super-resolution image quality by image correlation spectroscopy (QuICS)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elena Cerutti ◽  
Morgana D’Amico ◽  
Isotta Cainero ◽  
Gaetano Ivan Dellino ◽  
Mario Faretta ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Image Contrast ◽  
Correlation Spectroscopy ◽  
Image Correlation ◽  
Signal To Noise ◽  
Image Correlation Spectroscopy ◽  
Noise Ratio

AbstractQuantifying the imaging performances in an unbiased way is of outmost importance in super-resolution microscopy. Here, we describe an algorithm based on image correlation spectroscopy (ICS) that can be used to assess the quality of super-resolution images. The algorithm is based on the calculation of an autocorrelation function and provides three different parameters: the width of the autocorrelation function, related to the spatial resolution; the brightness, related to the image contrast; the relative noise variance, related to the signal-to-noise ratio of the image. We use this algorithm to evaluate the quality of stimulated emission depletion (STED) images of DNA replication foci in U937 cells acquired under different imaging conditions. Increasing the STED depletion power improves the resolution but may reduce the image contrast. Increasing the number of line averages improves the signal-to-noise ratio but facilitates the onset of photobleaching and subsequent reduction of the image contrast. Finally, we evaluate the performances of two different separation of photons by lifetime tuning (SPLIT) approaches: the method of tunable STED depletion power and the commercially available Leica Tau-STED. We find that SPLIT provides an efficient way to improve the resolution and contrast in STED microscopy.

scholarly journals Design optimisation of permanently installed monitoring system for polycrystalline materials

2020 ◽  
pp. 147592172095019
Author(s):  
Yuan Liu ◽  
Peter B. Nagy ◽  
Peter Cawley
Keyword(s):  
Surface Area ◽  
Monitoring System ◽  
Signal To Noise Ratio ◽  
Coarse Grained ◽  
Polycrystalline Materials ◽  
Depth Range ◽  
Bulk Wave ◽  
Signal To Noise ◽  
Trade Off ◽  
Noise Ratio

This article presents a design procedure for structural health monitoring systems based on bulk wave ultrasonic sensors for structures fabricated from polycrystalline materials. When designing a monitoring system, maximum coverage per transducer is a general requirement in order for the system to be economic. For coarse-grained polycrystalline materials, monitoring is often made challenging by low signal-to-noise ratios caused by grain scattering. Therefore, when designing a monitoring system for these materials, in addition to the economic requirement, it needs to be ensured that an adequate signal-to-noise ratio can be obtained throughout the monitoring volume. This typically introduces a trade-off between volume coverage per transducer and sensitivity that must be investigated. In this article, this trade-off is studied and a methodology using signal-to-noise maps is presented to design the system, that is, choose the optimal transducer parameters and placement. First, a combined analytical and numerical approach is used to generate a signal-to-noise map. Then, the influence of various factors on signal-to-noise ratio is investigated. Finally, two representative examples, with different criteria, are given to illustrate the methodology. In one example, the full surface area of the testpiece is covered with transducers and the optimum gives the deepest coverage. The other one aims to achieve the minimum fractional surface area that has to be covered with transducers to monitor a narrow depth range far from the surface, which has a potential application in weld monitoring. Results show that the optimum is likely to be at much lower frequency than typically used in inspection, as tracking signals with time gives sensitivity gains. Experiments were carried out to illustrate that higher volume coverage can be obtained at lower frequencies.

scholarly journals Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4076
Author(s):  
Yang ◽  
Zhu ◽  
Wang ◽  
Yang ◽  
Wu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Raman Spectrum ◽  
Background Noise ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Pseudo Color

Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.

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