Paper
2 July 1985 Theoretical Bases And Measurement Of The MTF Of Integrated Image Sensors
F. Chazallet, J. Glasser
Author Affiliations +
Proceedings Volume 0549, Image Quality: An Overview; (1985) https://doi.org/10.1117/12.948815
Event: 1985 Technical Symposium East, 1985, Arlington, United States
Abstract
By analogy with optics, the spatial resolution of image sensors is generally characterized by the Modulation Transfer Function (MTF). This notion assumes the system being a linear filter, which is not the case in integrated image sensors, since they have a discrete photoelement structure. These sensors must in fact be considered as integral samplers. Their response to any irradiance distribution can thus be computed, knowing the pitch of photoelements and using a characteristic function. This function is more or less similar to the MTF. Once exact theoretical foundations have been defined, a computer simulation enables the various MTF measuring methods to be compared this makes it possible to rule out er-rors inherent to experiments. The most accurate and reliable method appears to be the knife edge method, applied with a relative displacement of the sensor and of the image. This avoids the occurence of aliasing phenomenon. Experimentation of this method for measurement of the CCD sensors characteristic function, which we call MTF as agreed, is described. This method also makes it possible to evaluate the transfer inefficiency of shift registers.
© (1985) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
F. Chazallet and J. Glasser "Theoretical Bases And Measurement Of The MTF Of Integrated Image Sensors", Proc. SPIE 0549, Image Quality: An Overview, (2 July 1985); https://doi.org/10.1117/12.948815
Lens.org Logo
CITATIONS
Cited by 17 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Modulation transfer functions

Sensors

Image sensors

Linear filtering

Computer simulations

Fourier transforms

Image quality

Back to Top