Skip to main content
Article thumbnail
Location of Repository

Random Lens Imaging

By Rob Fergus, Antonio Torralba and William T. Freeman


We call a random lens one for which the function relating the input light ray to the output sensor location is pseudo-random. Imaging systems with random lensescan expand the space of possible camera designs, allowing new trade-offs in optical design and potentially adding new imaging capabilities. Machine learningmethods are critical for both camera calibration and image reconstruction from the sensor data. We develop the theory and compare two different methods for calibration and reconstruction: an MAP approach, and basis pursuit from compressive sensing. We show proof-of-concept experimental results from a random lens made from a multi-faceted mirror, showing successful calibration and image reconstruction. We illustrate the potential for super-resolution and 3D imaging

Year: 2006
OAI identifier:
Provided by: DSpace@MIT

Suggested articles

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.