PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
October 26, 2015IEEE Journal of Oceanic Engineering1,694 citations

Human-Visual-System-Inspired Underwater Image Quality Measures

View Full Paper
KPKaren PanettaKunming University of Science and TechnologyCGChen GaoDongguan University of TechnologySASos С. AgaianThe Graduate Center, CUNY

Key Points

Key points are not available for this paper at this time.

Abstract

Underwater images suffer from blurring effects, low contrast, and grayed out colors due to the absorption and scattering effects under the water. Many image enhancement algorithms for improving the visual quality of underwater images have been developed. Unfortunately, no well-accepted objective measure exists that can evaluate the quality of underwater images similar to human perception. Predominant underwater image processing algorithms use either a subjective evaluation, which is time consuming and biased, or a generic image quality measure, which fails to consider the properties of underwater images. To address this problem, a new nonreference underwater image quality measure (UIQM) is presented in this paper. The UIQM comprises three underwater image attribute measures: the underwater image colorfulness measure (UICM), the underwater image sharpness measure (UISM), and the underwater image contrast measure (UIConM). Each attribute is selected for evaluating one aspect of the underwater image degradation, and each presented attribute measure is inspired by the properties of human visual systems (HVSs). The experimental results demonstrate that the measures effectively evaluate the underwater image quality in accordance with the human perceptions. These measures are also used on the AirAsia 8501 wreckage images to show their importance in practical applications.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Panetta et al. (2015) studied this question.

synapsesocial.com/papers/69ce23856b0ac1c563d1857fhttps://doi.org/10.1109/joe.2015.2469915
Ask AI
Helpful
Bookmark
Share
View Full Paper