Paper
31 October 1997 Reconstruction of step edges with subpixel accuracy in gray-level images
Jose M. Sebastian y Zuniga, Oscar Reinoso, Rafael Aracil, David Garcia, Fernando Torres-Medina
Author Affiliations +
Abstract
Many inspection methods using computer vision have been developed, although in many of them the results obtained do not have the desired accuracy. In order to increase the system precision, two different solutions can be considered: the use of a more powerful image acquisition equipment and a solution that consists in developing algorithms that allow us to increase the accuracy of certain characteristics of the image. This paper is focused on setting a model that takes into account all the different signals involved in the image processing. It also defines the basis for the reconstruction of images in those areas with high content of information, such as edges, and more specifically those edges with a high change of intensity. In the image acquisition process, the input information is perfectly defined in a continuous domain and a discrete image is obtained as output, although distorted by the effects of the lenses, electrical sensor and the digitizer. This paper defines the conditions that the sampling process must satisfy in order to make possible the reconstruction of step edges using non-linear reconstruction filters in gray level images.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jose M. Sebastian y Zuniga, Oscar Reinoso, Rafael Aracil, David Garcia, and Fernando Torres-Medina "Reconstruction of step edges with subpixel accuracy in gray-level images", Proc. SPIE 3170, Image Reconstruction and Restoration II, (31 October 1997); https://doi.org/10.1117/12.279715
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image processing

Algorithm development

Image acquisition

Nonlinear filtering

Computer vision technology

Data processing

Image sensors

RELATED CONTENT


Back to Top