Sep 30, 2021 · In this paper, we propose a video super-resolution deep learning framework to achieve noise resilient video frames, which only need to be trained on the clean ...
In this paper, we propose a noise-robust VSR network which only needs to be trained on the clean images. That is, in our deep network for VSR, the model can ...
In this paper, a Noise-Robust Convolutional Neural Network (NR-CNN) is proposed to classify the noisy images without any preprocessing for noise removal.
ABSTRACT. Low-quality videos often not only have limited resolution, but also suffer from noise. Directly up-sampling a video without considering noise ...
Dec 25, 2020 · Deep learning models can employ various mathematical techniques to enhance noise robustness. Two commonly used techniques are denoising ...
May 17, 2024 · We propose the joint video denoising and super-resolution network for IoT cameras, which consists of the noise-robust moving-attention (NRMA) module and the ...
Jul 4, 2024 · In this study, we strengthen the video features by learning three temporal dynamics in video data: context order, playback direction, and the speed of video ...
Noise-robust video super-resolution using an adaptive spatial-temporal filter. In this paper, we introduce a new interpolation-based super-resolution scheme ...
Missing: Training | Show results with:Training
In this paper, we present a novel robust framework for low-level vision tasks, including denoising, object removal, frame interpolation, ...
In practice, we show that a single model learns photographic noise removal, denois- ing synthetic Monte Carlo images, and reconstruc- tion of undersampled MRI ...