In super-resolution (SR), a set of degraded low-resolution (LR) images are used to reconstruct a higher-resolution image that suffers from acquisition degradations. One way to boost SR images visual quality is to use restoration filters to remove reconstructed images artifacts. We propose an efficient method to optimally allocate the LR pixels on the high-resolution grid and introduce a mathematical derivation of a stochastic Wiener filter. It relies on the continuous-discrete-continuous model and is constrained by the periodic and nonperiodic interrelationships between the different frequency components of the proposed SR system. We analyze an end-to-end model and formulate the Wiener filter as a function of the parameters associated with the proposed SR system such as image gathering and display response indices, system average signal-to-noise ratio, and inter-subpixel shifts between the LR images. Simulation and experimental results demonstrate that the derived Wiener filter with the optimal allocation of LR images results in sharper reconstruction. When compared with other SR techniques, our approach outperforms them in both quality and computational time.
Copyright 2012 Society of Photo‑Optical Instrumentation Engineers (SPIE).
One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.
Original Publication Citation
Yousef, A., Li, J., & Karim, M. (2012). Mathematical model development of super-resolution image Wiener restoration. Optical Engineering, 51(3), 1-12, Article 037007. https://doi.org/10.1117/1.OE.51.3.037007
Yousef, Amr H.; Li, Jiang; and Karim, Mohammad A., "Mathematical Model Development of Super-Resolution Image Wiener Restoration" (2012). Electrical & Computer Engineering Faculty Publications. 397.