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- W3110408971 abstract "This Nowadays, deep learning is one of the most used technique for image denoising until it outperforms so far, all other denoising methods. However, this method requires a lot of computing power, so it’s quite difficult to achieve real-time deep learning denoisers especially on edge devices like embedded systems and mobile phones. In this paper, we proposed a deep learning denoiser that works in real-time on a Raspberry Pi 3B+, 1GB of ram, to increase in real-time the incoming noisy video from a Raspberry Pi Camera frame per frame, where each frame is an RGB image if size 256x256. We used a residual denoiser that extracts the noise and enhance the quality of obtained images. In fact, the proposed architecture has a very small size that can fit easily on any edge device. Furthermore, many optimization techniques were applied on the denoiser so it can run faster on a very limited computing resource. Each denoised frame where uploaded directly to a Microsoft storage service." @default.
- W3110408971 created "2020-12-07" @default.
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- W3110408971 date "2020-10-01" @default.
- W3110408971 modified "2023-10-18" @default.
- W3110408971 title "Residual Convolutional Neural Networks Model For Image Denoising On Real Time" @default.
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- W3110408971 doi "https://doi.org/10.1109/iccad49821.2020.9260531" @default.
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