Clustering Based Approach for Underwater Sunlight Flicker Removal

Tripathy, Abhijeet; Singh, Malkhan; Roy, Haraprasad

doi:10.1109/ieeeconf38699.2020.9389123

Cited by 1 publication

(1 citation statement)

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“…This proposed solution was extensively evaluated in terms of keypoint detection, image matching, and 3-D reconstruction performance in [1]. Tripathy et al [23] presented a clustering-based approach for underwater sunlight flicker removal. As in some other works, their method is based on temporal median calculation and thresholding to be used as an online algorithm for AUVs to minimize the sunlight flicker distortions in the frames during the video survey and was tested in a controlled pool environment.…”

Section: B Related Work On Caustics Detection and Removalmentioning

confidence: 99%

Seafloor-Invariant Caustics Removal From Underwater Imagery

Agrafiotis

Καράντζαλος

Georgopoulos

2023

IEEE J. Oceanic Eng.

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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 degrades image quality and affects severely any 2-D mosaicking or 3-D reconstruction of the seabed. In this article, 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 the seabed and riverbed of any anaglyph, correcting the images using real pixel information, thus, improving image matching and 3-D reconstruction processes. In particular, the developed method employs deep learning architectures to classify image pixels to "noncaustics" and "caustics." Then, it exploits the 3-D geometry of the scene to achieve a pixelwise 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 data set, 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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Section: B Related Work On Caustics Detection and Removalmentioning

confidence: 99%