Lens correction algorithm based on the see-saw diagram to correct Seidel aberrations employing aspheric surfaces

Physics – Optics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

In this paper a lens correction algorithm based on the see- saw diagram developed by Burch is described. The see-saw diagram describes the image correction in rotationally symmetric systems over a finite field of view by means of aspherics surfaces. The algorithm is applied to the design of some basic telescopic configurations such as the classical Cassegrain telescope, the Dall-Kirkham telescope, the Pressman-Camichel telescope and the Ritchey-Chretien telescope in order to show a physically visualizable concept of image correction for optical systems that employ aspheric surfaces. By using the see-saw method the student can visualize the different possible configurations of such telescopes as well as their performances and also the student will be able to understand that it is not always possible to correct more primary aberrations by aspherizing more surfaces.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Lens correction algorithm based on the see-saw diagram to correct Seidel aberrations employing aspheric surfaces does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Lens correction algorithm based on the see-saw diagram to correct Seidel aberrations employing aspheric surfaces, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Lens correction algorithm based on the see-saw diagram to correct Seidel aberrations employing aspheric surfaces will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-1248407

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.