Open AccessSingle‐frame image super‐resolution inspired by perceptual criteria
Author(s) -
Zhou Fei,
Liao Qingmin
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2013.0808
Subject(s) - principal component analysis , pattern recognition (psychology) , artificial intelligence , similarity (geometry) , feature (linguistics) , perception , frame (networking) , mathematics , mean squared error , function (biology) , object (grammar) , task (project management) , computer science , image quality , image (mathematics) , computer vision , statistics , engineering , psychology , telecommunications , philosophy , linguistics , neuroscience , evolutionary biology , biology , systems engineering
In this study, the authors consider the problem of image super‐resolution (SR) in terms of the perceptual criteria. Existing SR methods treat the traditional mean‐squared error (MSE) as an irreplaceable objective function. However, MSE has been widely criticised since it is inconsistent with visual perception of human beings. The perceptual criteria, including the structural similarity (SSIM) index and feature similarity (FSIM) index, have been reported to be more effective in assessing image quality. Therefore SSIM and FSIM are included for the SR task in this study. Specifically, the authors first propose to reform principal component analysis (PCA), which is named as visual perceptual PCA (VP‐PCA), by adopting SSIM as the object function. Subsequently, to accomplish the SR task, the authors cluster the training data and perform VP‐PCA on each cluster to calculate the coefficients. Finally, based on the principle of FSIM, the traditional SR results and the SR results using VP‐PCA are combined to form our fused results. Experimental results are provided to show the superiority of the proposed method over several state‐of‐the‐art methods in both quantitative and visual comparisons.