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- W3028472502 abstract "A common quality impairment observed in surgery videos is blur, caused by object motion or a defocused camera. Degraded image quality hampers the progress of machine-learning-based approaches in learning and recognizing semantic information in surgical video frames like instruments, phases, and surgical actions. This problem can be mitigated by automatically deblurring video frames as a preprocessing method for any subsequent video analysis task. In this paper, we propose and evaluate a multi-scale deconvolutional neural network to deblur cataract surgery videos. Experimental results confirm the effectiveness of the proposed approach in terms of the visual quality of frames as well as PSNR improvement." @default.
- W3028472502 created "2020-05-29" @default.
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- W3028472502 date "2020-04-01" @default.
- W3028472502 modified "2023-09-27" @default.
- W3028472502 title "Deblurring Cataract Surgery Videos Using a Multi-Scale Deconvolutional Neural Network" @default.
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- W3028472502 doi "https://doi.org/10.1109/isbi45749.2020.9098318" @default.
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