Super-resolution image reconstruction techniques: Trade-offs between the data-fidelity and regularization terms

Panagiotopoulou, Antigoni

Anastassopoulos, Vassilis

Παναγιωτοπούλου, Αντιγόνη

Αναστασόπουλος, Βασίλειος

Stochastic regularized methods are quite advantageous in Super-Resolution (SR) image reconstruction problems. In the particular techniques the SR problem is formulated by means of two terms, the data-fidelity term and the regularization term. The present work examines the effect of each one of these terms on the SR reconstruction result with respect to the presence or absence of noise in the Low-Resolution (LR) frames. Experimentation is carried out with the widely employed L2, L1, Huber and Lorentzian estimators for the data-fidelity term. The Tikhonov and Bilateral (B) Total Variation (TV) techniques are employed for the regularization term. The extracted conclusions can, in practice, help to select an effective SR method for a given sequence of LR frames. Thus, in case that the potential methods present common data-fidelity or regularization term, and frames are noiseless, the method which employs the most robust regularization or data-fidelity term should be used. Otherwise, experimental conclusions regarding performance ranking vary with the presence of noise in frames, the noise model as well as the difference in robustness of efficiency between the rival terms. Estimators employed for the data-fidelity term or regularizations stand for the rival terms.

Journal (paper)

Method selection

Super-resolution

Noisy frames

Regularization

Data-fidelity

University of Patras

Nemertes

http://dx.doi.org/10.1016/j.inffus.2010.11.005

Information Fusion

English

2011-12-08T09:38:22Z

2010-12-07

2011-12-08

http://hdl.handle.net/10889/4841

Information Fusion

NOTICE: this is the author’s version of a work that was accepted for publication in Information Fusion. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Fusion, in press, http://dx.doi.org/10.1016/j.inffus.2010.11.005.