Abstract

The technique of reconstructing a higher-resolution (HR) image of size $ML\times ML$ by digitally processing $L\times L$ subpixel-shifted lower-resolution (LR) copies of it, each of size $M\times M$, has now become well established. This particular digital superresolution problem is analyzed from the standpoint of the generalized sampling theorem. It is shown both theoretically and by computer simulation that the choice of regularly spaced subpixel shifts for the LR images tends to maximize the robustness and minimize the error of reconstruction of the HR image. In practice, since subpixel-level control of LR image shifts may be nearly impossible to achieve, however, a more likely scenario, which is also discussed, is one involving random subpixel shifts. It is shown that without reasonably tight bounds on the range of random shifts, the reconstruction is likely to fail in the presence of even small amounts of noise unless either reliable prior information or additional data are available.

© 2007 Optical Society of America

Robust and computationally efficient superresolution algorithm

Kaggere V. Suresh and Ambasamudram N. Rajagopalan
J. Opt. Soc. Am. A 24(4) 984-992 (2007)

Design of an image restoration algorithm for the TOMBO imaging system

Shachar Mendelowitz, Iftach Klapp, and David Mendlovic
J. Opt. Soc. Am. A 30(6) 1193-1204 (2013)

Nonuniform sampling, image recovery from sparse data and the discrete sampling theorem

Leonid P. Yaroslavsky, Gil Shabat, Benjamin G. Salomon, Ianir A. Ideses, and Barak Fishbain
J. Opt. Soc. Am. A 26(3) 566-575 (2009)

Spectral-overlap approach to multiframe superresolution image reconstruction

Edward Cohen, Richard H. Picard, and Peter N. Crabtree
Appl. Opt. 55(15) 3978-3992 (2016)

Improved-resolution digital holography using the generalized sampling theorem for locally band-limited fields

Adrian Stern and Bahram Javidi
J. Opt. Soc. Am. A 23(5) 1227-1235 (2006)