Super-resolution Image Reconstruction Sina Jahanbin Richard Naething EE381K-14 March 10, 2005

Problem Statement There is a limit to the spatial resolution that can be recorded by any digital device. This may be due to: optical distortions motion blur under-sampling noise

Introduction to Super-resolution (SR) Reconstruction Techniques SR image reconstruction is the process of combining several low resolution (LR) images into a single higher resolution (HR) image.

“Restoration of a Single Superresolution Image from Several Blurred, Noisy, and Undersampled Measured Images”[Elad & Feuer, 1997] Three main tools in single image restoration Maximum likelihood (ML) estimator Maximum a posteriori (MAP) Projection onto convex sets (POCS) This paper takes these existing single image restoration techniques and applies them to SR A hybrid algorithm has been proposed that combines the ML estimator and POCS

“Superresolution Video Reconstruction with Arbitrary Sampling Lattices and Nonzero Aperture Time” [Patti, Sezan, & Murat, 1997] Uses a model that takes into account details ignored by previous SR models Arbitrary sampling lattice Sensor element’s physical dimensions Aperture time Focus blurring Additive noise

“Limits on Super-Resolution and How to Break Them” [Baker & Kanade, 2002] Assumes image registration has already been accomplished and focuses on fusing step – or combining multiple aligned LR images into HR image Uses what the authors call a “hallucination” or “recogstruction” algorithm Claims significantly better results – both subjectively and in RMS pixel error

Future Work Many papers on SR base their results on subjective viewing of images or use an objective measurement, such as RMS, that in many applications is not meaningful. We propose to develop an objective measure of SR methods that has a basis in real world application performance.