Research Area: Machine Learning
Underwater imaging is one of the hot areas of research and is receiving considerable attention from the research community due to the challenges involved. Underwater images are prone to various distortions like poor contrast and colour deviation. Light scattering and light absorption in water medium are the main reasons for degradation in subaquatic images. Scattering of visible-light energy reduces the sharpness of image whereas varying degrees of light attenuation travelling in water results in the colour change. Restoration of distorted underwater images is an ill-posed and challenging problem. Various techniques and methodologies are being used to process and restore underwater images. In this study, we present a state-of-the-art review of various conventional and computer vision-based algorithms and techniques, developed so far, to present a clearer view of the methods used for underwater image restoration. We discuss various conditions for which the schemes have been developed as well as highlight the quality assessment methods used to evaluate their performance. We compare various state-of-art schemes based on various subjective and objective indices and discuss future research directions in the field of underwater image restoration.
Keywords:
Underwater image
restoration
Light scattering
Machine learning
Author(s) Name: Sheezan Fayaz, Shabir A. Parah, G. J. Qureshi, Vijaya Kumar
Journal name: IET Image Processing
Conferrence name:
Publisher name: Wiley
DOI: 10.1049/ipr2.12041
Volume Information: Volume15, Issue2
