Seafloor-Invariant Caustics Removal from Underwater Imagery

• URL: http://arxiv.org/abs/2212.10167v1
• Date: Tue, 20 Dec 2022 11:11:02 GMT
• Title: Seafloor-Invariant Caustics Removal from Underwater Imagery
• Authors: Panagiotis Agrafiotis, Konstantinos Karantzalos, Andreas Georgopoulos
• Abstract summary: Caustics are complex physical phenomena resulting from the projection of light rays being refracted by the wavy surface. In this work, we propose a novel method for correcting the effects of caustics on shallow underwater imagery. In particular, the developed method employs deep learning architectures in order to classify image pixels to "non-caustics" and "caustics"
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Mapping the seafloor with underwater imaging cameras is of significant importance for various applications including marine engineering, geology, geomorphology, archaeology and biology. For shallow waters, among the underwater imaging challenges, caustics i.e., the complex physical phenomena resulting from the projection of light rays being refracted by the wavy surface, is likely the most crucial one. Caustics is the main factor during underwater imaging campaigns that massively degrade image quality and affect severely any 2D mosaicking or 3D reconstruction of the seabed. In this work, we propose a novel method for correcting the radiometric effects of caustics on shallow underwater imagery. Contrary to the state-of-the-art, the developed method can handle seabed and riverbed of any anaglyph, correcting the images using real pixel information, thus, improving image matching and 3D reconstruction processes. In particular, the developed method employs deep learning architectures in order to classify image pixels to "non-caustics" and "caustics". Then, exploits the 3D geometry of the scene to achieve a pixel-wise correction, by transferring appropriate color values between the overlapping underwater images. Moreover, to fill the current gap, we have collected, annotated and structured a real-world caustic dataset, namely R-CAUSTIC, which is openly available. Overall, based on the experimental results and validation the developed methodology is quite promising in both detecting caustics and reconstructing their intensity.

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