Implementation, validation and application of a tool for the assessment of the modulation transfer function and noise power spectrum of dental CBCT scanners

2020 ◽  
Author(s):  
Karen Merken ◽  
Guozhi Zhang ◽  
Eman Shaheen ◽  
Reinhilde Jacobs ◽  
Constantinus Politits ◽  
...  
Keyword(s):  
Transfer Function ◽  
Power Spectrum ◽  
Modulation Transfer Function ◽  
Noise Power ◽  
Noise Power Spectrum ◽  
Modulation Transfer

scholarly journals Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems

2020 ◽  
Vol 2020 (9) ◽  
pp. 345-1-345-7
Author(s):  
Edward W. S. Fry ◽  
Sophie Triantaphillidou ◽  
Robin B. Jenkin ◽  
Ralph E. Jacobson ◽  
John R. Jarvis
Keyword(s):  
Power Spectrum ◽  
Transfer Functions ◽  
Noise Power ◽  
Noise Power Spectrum ◽  
Modulation Transfer ◽  
Simulated Image ◽  
Image Capture ◽  
System Noise ◽  
Relative Standard ◽  
Average System

The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply nonlinear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The sceneand- process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-processdependent Modulation Transfer Functions (SPD-MTF).

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