Comprehensive evaluation of image enhancement for unsupervised image description and matching. Issue 16 (18th February 2021)

• Record Type: Journal Article
• Authors: Lecca, Michela; Torresani, Alessandro; Remondino, Fabio
• Is Part Of: IET image processing. Volume 14:Issue 16(2020)
• Page Start: 4329
• Page End: 4339
• Publication Date: 2021-02-18
• Subjects: feature extraction -- computer vision -- image enhancement -- transforms -- object detection -- image matching -- unsupervised learning; image enhancement -- unsupervised image description -- perceptual evaluation -- human perception -- image content -- functional evaluation -- enhancer effects -- specific machine application -- comprehensive evaluation -- empirical evaluation -- undesired illumination effects -- key‐point based image description -- matching task -- Scale Invariant Feature Trasform -- perceptual features -- input image -- ORB performance -- perceptual measures -- functional measures -- enhancement techniques -- mage description -- unsupervised image matching -- computer vision -- ORB algorithms -- oriented fast and rotated brief algorithms -- SIFT performance; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.1129
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The performance of an image enhancer is usually evaluated either perceptually or functionally. The perceptual evaluation is carried out from a human perspective, i.e. by considering features related to human perception of image content and details. The functional evaluation is made instead from a machine perspective, i.e. by judging the enhancer effects with a specific machine application. his work proposes a comprehensive, empirical evaluation accounting for both perceptual and functional aspects. Precisely, 13 enhancers lowering undesired illumination effects are considered within the keypoint based image description and matching task, which is relevant to many computer vision fields. Each enhancer is first evaluated perceptually, then it is employed as a pre‐processing step of the popular algorithms SIFT and ORB and judged by measuring how its use influences the performance of these algorithms. his study, conducted on a freely available data set, shows that the enhancement generally improves the perceptual features of the input image as well as the SIFT and ORB performance. More importantly, it reveals the existence of a correlation among some of their perceptual and functional measures. In this way, this work contributes to promote a more aware use of enhancement techniques within the mage description and matching task.