In this paper, we propose a video enhancement method using generative adversarial networks to remove raindrops and restore the background information on the removed region in the coastal wave video imagery distorted by raindrops during rainfall. Two experimental models are implemented: Pix2Pix network widely used for image-to-image translation and Attentive GAN, which is currently performing well for raindrop removal on a single images. The models are trained with a public dataset of paired natural images with and without raindrops and the trained models are evaluated their performance of raindrop removal and background information recovery of rainwater distortion of coastal wave video imagery. In order to improve the performance, we have acquired paired video dataset with and without raindrops at the real coast and conducted transfer learning to the pretrained models with those new dataset. The performance of fine-tuned models is improved by comparing the results from pretrained models. The performance is evaluated using the peak signal-to-noise ratio and structural similarity index and the finetuned Pix2Pix network by transfer learning shows the best performance to reconstruct distorted coastal wave video imagery by raindrops.
In this paper, we propose a hydrodynamic scene separation method for wave propagation from video imagery using autoencoder. In the coastal area, image analysis methods such as particle tracking and optical flow with video imagery are usually applied to measure ocean waves owing to some difficulties of direct wave observation using sensors. However, external factors such as ambient light and weather conditions considerably hamper accurate wave analysis in coastal video imagery. The proposed method extracts hydrodynamic scenes by separating only the wave motions through minimizing the effect of ambient light during wave propagation. We have visually confirmed that the separation of hydrodynamic scenes is reasonably well extracted from the ambient light and backgrounds in the two videos datasets acquired from real beach and wave flume experiments. In addition, the latent representation of the original video imagery obtained through the latent representation learning by the variational autoencoder was dominantly determined by ambient light and backgrounds, while the hydrodynamic scenes of wave propagation independently expressed well regardless of the external factors.
Intraoperative neurophysiological monitoring during aneurysm surgery can reduce the risk of ischemic insults and can improve neurological prognoses by detecting abnormal findings in real time when cerebral circulation is at risk. In aneurysm clipping surgery, ischemic insults are most likely to occur at the clipping step. Somatosensory evoked potentials (SSEPs) are known to be the most sensitive method for detecting ischemic insults. However, there are several cases in which SSEP findings and the postoperative prognosis are inconsistent. we report a patient who showed postoperative neurologic deficits after posterior communicating artery (P-com) aneurysm clipping surgery, even though the amplitude of the SSEP had fully recovered shortly after the point at which it had decreased by 15%. The patient's brain MRI revealed an anterior thalamic infarction, and transfemoral cerebral angiography showed an anterior thalamoperforating artery occlusion. This case shows that if a thalamic infarction occurs in the anterior region (not in the posterolateral region, which is involved in the somatosensory pathway), then it may not be identified by SSEP.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.