论文标题
使用可变形的卷积残留块和自我注意
Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention
论文作者
论文摘要
尽管深度学习对图像/视频恢复和超分辨率产生了重大影响，但到目前为止，学到的开采裂纹在学术界或行业中受到了较少的关注。尽管脱位模型是已知和固定的，但它还是非常适合从合成数据中监督学习的。在本文中，我们提出了一个新颖的多场全帧速率Deinterallacing网络，该网络适应了最先进的超分辨率方法，以执行DeinterLacing Task。我们的模型使用可变形的卷积残留块和自我注意力对齐从相邻字段到参考字段（待解剖）的特征。我们广泛的实验结果表明，所提出的方法在数值和感知性能方面提供了最先进的开采结果。在撰写本文时，我们的模型在https://videprocessing.ai/benchmarks/deinterlacer.html中排名第一。
Although deep learning has made significant impact on image/video restoration and super-resolution, learned deinterlacing has so far received less attention in academia or industry. This is despite deinterlacing is well-suited for supervised learning from synthetic data since the degradation model is known and fixed. In this paper, we propose a novel multi-field full frame-rate deinterlacing network, which adapts the state-of-the-art superresolution approaches to the deinterlacing task. Our model aligns features from adjacent fields to a reference field (to be deinterlaced) using both deformable convolution residual blocks and self attention. Our extensive experimental results demonstrate that the proposed method provides state-of-the-art deinterlacing results in terms of both numerical and perceptual performance. At the time of writing, our model ranks first in the Full FrameRate LeaderBoard at https://videoprocessing.ai/benchmarks/deinterlacer.html
