New Algorithms for the Reduction of Image Degradation Due to Atmospheric Turbulence
Abstract
The problem of removing the deleterious effects of clear-air turbulence from images has been formally solved. As a spinoff, the method also allows wind velocity/shear to be measured along the optical line of sight. The overall approach uses two short-exposure images as inputs. These are divided in Fourier space. The division data is independent of the object (since it is the same in both images), and depends upon parameters that specify the two point spread functions. A neural net allows these parameters to be found, with good accuracy. The two images are then inverse-filtered, using the p.s.f. information. The average of the two inverse-filtered outputs is then the final reconstruction. Because of the neural net implementation, the entire algorithm can be emplaced upon a logic chip and used in quasi-real time. Future development would seem to promise real-time operation. Image reconstruction, Clear air turbulence, Smart camera
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 22, 1994
- Accession Number
- ADA284891
Entities
People
- B. R. Frieden
Organizations
- University of Arizona