1 June 2004 Modulation transfer function estimation from nonspecific images
Jean-Marc Delvit, Dominique Leger, Sylvie Roques, Christophe Valorge
Author Affiliations +
Abstract
Measurement of the modulation transfer function (MTF) to quantify the quality of an imaging system proves to be very important in the context of Earth observation satellites. In particular, this measurement is essential to carry out the focusing of the telescope, or to implement a deconvolution filter whose goal is to enhance the image contrast or to reduce the noise. Its knowledge also allows us to compare the characteristics of different known and unknown satellites. We suggest an univariate MTF measurement method using nonspecific views. First of all, the landscape has to be characterized to discriminate ground structure information from MTF information. Once this separation is carried out, landscape structure information can be extracted, allowing a classification between very uniform scenes and more structured ones. Then the MTF, which is described by a bidimensional analytical physical model, can be assessed using an artificial neural network. The principle is to use the artificial neural network to learn the MTF of simulated or perfectly known images, and then use it to assess the MTF of totally unknown images. We show that this method is robust even if noise is taken into account. As a result, maximum MTF assessment errors are less than 10%. This enables us to suggest further developments, including a general scheme for assessment of image quality.
©(2004) Society of Photo-Optical Instrumentation Engineers (SPIE)
Jean-Marc Delvit, Dominique Leger, Sylvie Roques, and Christophe Valorge "Modulation transfer function estimation from nonspecific images," Optical Engineering 43(6), (1 June 2004). https://doi.org/10.1117/1.1724838
Published: 1 June 2004
Lens.org Logo
CITATIONS
Cited by 20 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Modulation transfer functions

Error analysis

Neural networks

Optical engineering

Satellites

Image analysis

Artificial neural networks

RELATED CONTENT


Back to Top