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Found 2 papers, 1 papers with code
A geometry-aware deep network for depth estimation in monocular endoscopy
To address this issue, we introduce a gradient loss to penalize edge fluctuations ambiguous around stepped edge structures and a normal loss to explicitly express the sensitivity to frequently small structures, and propose a geometric consistency loss to spreads the spatial information across the sample grids to constrain the global geometric anatomy structures.
A Novel Underwater Image Enhancement and Improved Underwater Biological Detection Pipeline
For aquaculture resource evaluation and ecological environment monitoring, automatic detection and identification of marine organisms is critical.
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