title: An efficient selective perceptual-based super-resolution estimator.
abstract: In this paper, a selective perceptual-based (SELP) framework is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual human visual system model is proposed to compute local contrast sensitivity thresholds. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. Processing only a set of perceptually significant pixels reduces significantly the computational complexity of SR algorithms without losing the achievable visual quality. The proposed SELP framework is integrated into a maximum-a posteriori-based SR algorithm as well as a fast two-stage fusion-restoration SR estimator. Simulation results show a significant reduction on average in computational complexity with comparable signal-to-noise ratio gains and visual quality.
authors.affiliation: School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287-5706, USA. karam@asu.edu
authors.forename: Lina J
authors.initials: LJ
authors.lastname: Karam
authors.forename: Nabil G
authors.initials: NG
authors.lastname: Sadaka
authors.forename: Rony
authors.initials: R
authors.lastname: Ferzli
authors.forename: Zoran A
authors.initials: ZA
authors.lastname: Ivanovski