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Underwater image enhancement and turbidity removal (dehazing) is a very challenging problem, not only due to the sheer variety of environments where it is applicable, but also due to the lack of high-resolution, labelled image data. In this paper, we present a novel, two-step deep learning approach for underwater image dehazing and colour correction. In iDehaze, we leverage computer graphics to physically model light propagation in underwater conditions. Specifically, we construct a three-dimensional, photorealistic simulation of underwater environments, and use them to gather a large supervised training dataset. We then train a deep convolutional neural network to remove the haze in these images, then train a second network to transform the colour space of the dehazed images onto a target domain. Experiments demonstrate that our two-step iDehaze method is substantially more effective at producing high-quality underwater images, achieving state-of-the-art performance on multiple datasets. Code, data and benchmarks will be open sourced.
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Improving the Visibility of Underwater Video in Turbid Aqueous Environments
Water turbidity is a frequent impediment for achieving satisfactory imaging clarity in underwater video and inhibits the extraction of information concerning the condition of submerged structures. Ports, rivers, lakes, and inland waterways are notoriously difficult spots for camera inspections, in particular for hull inspections in lieu of dry-docking. This complex problem motivated us to study methods to extract a cleaner image /video footage from the acquired one. The purpose of this paper is to describe a novel mathematical model for the degradation of images due to underwater turbidity caused by suspended silt particulates and algae organisms and to propose methods to improve image and video clarity using multiscale non-linear transforms.
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- Award ID(s):
- 1720487
- PAR ID:
- 10293508
- Date Published:
- Journal Name:
- SNAME Maritime Convention
- Page Range / eLocation ID:
- SNAME-SMC-2020-090
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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