Some Simple Mathematical Methods For Derivation Of Optical Image Quality Data From Edge Sharpness

1977 ◽  
Author(s):  
I. Overington ◽  
M. B. Brown
Keyword(s):  
Image Quality ◽  
Optical Image ◽  
Quality Data ◽  
Mathematical Methods ◽  
Edge Sharpness

VIS-NIR LED Illumination in Backside Circuit Edit and Optical Probing Applications

2012 ◽  
Author(s):  
Shida Tan ◽  
Richard H. Livengood ◽  
Dane Scott ◽  
Roy Hallstein ◽  
Pat Pardy ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Quality ◽  
Optical Imaging ◽  
Narrow Band ◽  
Light Emitting Diode ◽  
Air Gap ◽  
Optical Image ◽  
Proof Of Concept ◽  
Light Emitting ◽  
Optical Probing

Abstract High resolution optical imaging is critical in assisting backside circuit edit (CE) and optical probing navigation. In this paper, we demonstrated improved optical image quality using VIS-NIR narrow band light emitting diode (LED) illumination in various FIB and optical probing platforms. The proof of concept was demonstrated with both common non-contact air gap lenses and solid immersion lenses (SIL).

scholarly journals Human optical image quality and the spatial standard observer

2004 ◽  
Vol 4 (11) ◽  
pp. 2-2 ◽  
Author(s):  
A. B. Watson ◽  
A. J. Ahumada
Keyword(s):  
Image Quality ◽  
Optical Image ◽  
Standard Observer

Optical image quality and the cone mosaic

Science ◽  
1986 ◽  
Vol 231 (4737) ◽  
pp. 499-501 ◽  
Author(s):  
A. Snyder ◽  
T. Bossomaier ◽  
A Hughes
Keyword(s):  
Image Quality ◽  
Optical Image ◽  
Cone Mosaic

Scatter and Its Implications for the Measurement of Optical Image Quality in Human Eyes

2003 ◽  
Vol 80 (1) ◽  
pp. 58-68 ◽  
Author(s):  
MICHAEL J. COX ◽  
DAVID A. ATCHISON ◽  
and DION H. SCOTT
Keyword(s):  
Image Quality ◽  
Optical Image ◽  
Human Eyes

scholarly journals Face Image Quality Assessment: A Literature Survey

2022 ◽  
Author(s):  
Torsten Schlett ◽  
Christian Rathgeb ◽  
Olaf Henniger ◽  
Javier Galbally ◽  
Julian Fierrez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Face Image ◽  
Quality Data ◽  
Learning Approaches ◽  
The Face ◽  
Future Work

The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.

Free-breathing contrast-enhanced multiphase MRI of the liver in patients with a high risk of breath-holding failure: comparison of compressed sensing-accelerated radial and Cartesian acquisition techniques

2021 ◽  
pp. 028418512110529
Author(s):  
Eun Sun Choi ◽  
Jin Sil Kim ◽  
Marcel Dominik Nickel ◽  
Jae Kon Sung ◽  
Jeong Kyong Lee
Keyword(s):  
Image Quality ◽  
Detection Rate ◽  
Free Breathing ◽  
Arterial Phase ◽  
Breath Holding ◽  
Diagnostic Quality ◽  
Contrast Enhanced ◽  
Edge Sharpness ◽  
Liver Edge

Background Knowing the advantages and disadvantages of each magnetic resonance (MR) technique, would allow us to choose a sequence better suited in patients with a high risk of breath-holding failure. Purpose To compare the image quality of free-breathing contrast-enhanced multiphase MR imaging (MRI) using incoherent Cartesian k-space sampling combined with a motion-resolved compressed sensing reconstruction (XD-VIBE) and Golden-Angle Radial Sparse Parallel MRI (GRASP). Material and Methods A total of 67 patients were included. Overall image quality, motion artifacts, and liver edge sharpness on arterial and portal-venous phase were evaluated by two radiologists. We evaluated the signal intensity ratio between liver in the late arterial phase to aorta at peak enhancement and the detection rate of hypervascular lesions. Results Overall image quality, artifact, and liver edge sharpness scores of XD-VIBE and GRASP were not significantly different ( P = 0.070–0.397). Four (reviewer 1, 12.1%) and seven patients (reviewer 2, 21.2%) received non-diagnostic quality in the XD-VIBE group whereas one patient (reviewer 2, 2.9%) received non-diagnostic quality in the GRASP group. The ratio between the aorta and liver signal for GRASP was significantly higher than that of XD-VIBE (0.32 ± 0.10 vs. 0.47 ± 0.13; P < 0.001). The hypervascular lesion detection rate of XD-VIBE (86.7%) was higher than that of GRASP (57.1%) in the arterial phase without a statistically significant difference ( P = 0.081). Conclusion Overall image quality of XD-VIBE and GRASP were not significantly different. More XD-VIBE examinations were rated non-diagnostic. On the other hand, the relative liver parenchymal enhancement to the aorta in the late arterial phase of GRASP was higher than that of XD-VIBE, which potentially leads to lower detectability of hypervascular lesions on arterial phase images.

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