FastDVDnet: Towards Real-Time Video Denoising Without Explicit Motion Estimation

In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Until recently, video denoising with neural networks had been a largely under explored domain, and existing methods could not compete with the performance of the best patch-based methods. The approach we introduce in this paper, called FastDVDnet, shows similar or better performance than other state-of-the-art competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as fast run-times, and the ability to handle a wide range of noise levels with a single network model. The characteristics of its architecture make it possible to avoid using a costly motion compensation stage while achieving excellent performance. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics

Coastal morphodynamic emulator for early warning short-term forecasts

- Name of the Software: XBeach version 1.23. Developers: Deltares/XBeach Open-Source Community; First year available: 2009; Cost: Free; Software availability:https://download.deltares.nl/en/download/xbeach-open-source/; Program size: 330.97 MB. - The deep learning-based emulator used for surrogating the XBeach morphodynamic module was implemented in Python language (version 3.9) based on TensorFlow library. The authors used a Windows 11 Home OS environment, CPU Intel(R) Core (TM) i7-8750H 2.20 GHz, RAM 16 GB, GPU Nvidia GeForce GTX 1060. The architecture of the model is available at: http://www.hydroshare.org/resource/b4ae97df748842a1800816b32a3d640 b.Data will be made available on request. Deep learning model for XBeach morphodynamic emulation (Original data) (HydroShare): https://www.hydroshare.org/resource/b4ae97df748842a1800816b32a3d640b/The use of numerical models to anticipate the effects of floods and storms in coastal regions is essential to mitigate the damages of these natural disasters. However, local studies require high spatial and temporal resolution numerical models, limiting their use due to the involved high computational costs. This constraint becomes even more critical when these models are used for real-time monitoring and warning systems. Therefore, the objective of this paper was to reduce the computational time of coastal morphodynamic models simulations by implementing a deep learning emulator. The emulator performance was evaluated using different scenarios run with the XBeach software, which considered different grid resolutions and the effects of a storm event in the morphodynamic patterns around a breakwater and a groin. The morphodynamic simulation time was reduced by 23%, and it was identified that the major restriction to reducing the computational cost was the hydrodynamic numerical model simulation.This research was supported by the Doctoral Grant SFRH/BD/151383/2021 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from the Ministry of Science, Technology and Higher Education, under the MIT Portugal Program. I. Iglesias also acknowledge the FCT financing through the CEEC program (2022.07420. CEECIND)