（VESPCN）Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
一、简介 1、目的 作者的目的是引进一个spatio-temporal sub-pixel convolution networks，能够处理视频图像超分辨，并且做到实时速度。还提出了一个将动作补偿和视频超分辨联合起来的算法，并且可以端到端训练。 与单帧模型相比，时空网络即可以减少计算，又可以维持输出质量。 2、introduction 超分辨问题一直是信号处理领域中的一大挑战。在多帧超分辨问题
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相关文章
- 1. Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation 论文笔记
- 2. Spatiotemporal Residual Networks for Video Action Recognition
- 3. STSN：Object Detection in Video with Spatiotemporal Sampling Networks
- 4. Spatiotemporal Multiplier Networks for Video Action Recognition
- 5. 视频超分——02 VESPCN
- 6. 论文阅读笔记（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）
- 7. 论文阅读《Spatiotemporal Multiplier Networks for Video Action Recognition》
- 8. Appearance-and-Relation Networks for Video Classification
- 9. Image and Video Compression Techniques
- 10. 论文阅读：EDVR: Video Restoration with Enhanced Deformable Convolutional Networks