Symmetric Logarithmic Derivative Eigen-projection Adaptive Algorithm for Super-Resolution Imaging

Case ID:

This technology describes an eigen-projection adaptive algorithm that can be used for super-resolution imaging in a variety of contexts, including life-sciences microscopy or astronomy.

Traditional advanced microscopy or telescopy uses several different measuring methods to estimate brightness, deduce position, and discriminate between object bodies. These methods work well, but often there must be additional technology or algorithms deployed to enhance the resolution.

In this technology, the inventors propose a new and improved adaptive algorithm that can yield super-resolution imaging in a variety of contexts. They propose to do this by having developed a symmetric logarithmic derivative eigen-projection adaptive algorithm. The algorithm works by its two main stages of initialization and establishing the symmetric logarithmic derivative (SLD) eigen-projection.


  • Super-resolution life sciences or subcellular microscopy
  • Telescopic devices for astronomical or extra-galactic imaging and viewing


  • Super-resolution
  • Adaptive algorithm
Patent Information:
Contact For More Information:
Richard Weite
Senior Licensing Manager, College of Optical Sciences
The University of Arizona
Lead Inventor(s):
Kwan Kit Lee
Michael Grace
Amit Ashok
Saikat Guha