用幽灵特征减少视频超分冗余，提升细节保留
GRNN:Recurrent Neural Network based on Ghost Features for Video Super-Resolution
- 引入幽灵特征机制，压缩冗余信息降低计算开销
- 在多个数据集上实现PSNR和SSIM小幅提升，纹理更清晰
- 适合关注高效视频超分与时序建模的开发者
基于卷积神经网络（CNN）的现代视频超分辨率（VSR）系统计算成本高昂。现有模型普遍存在特征冗余问题，但在VSR领域较少被讨论。我们实验发现VSR模型中大量特征高度相似，因此提出使用“幽灵特征”（Ghost features）来缓解此问题。同时，分析了传统循环卷积网络（RNN）中的“梯度消失”现象，并将幽灵模块与RNN结合，实现对时序信息的有效建模。当前帧与下一帧作为输入，结合前一帧输出及隐藏状态进行推理。在多个基准模型与数据集上的大量实验表明，所提方法在PSNR和SSIM上均有提升，视频纹理细节也得到更好保留。
原文摘要 · Abstract (English)
Modern video super-resolution (VSR) systems based on convolutional neural networks (CNNs) require huge computational costs. The problem of feature redundancy is present in most models in many domains, but is rarely discussed in VSR. We experimentally observe that many features in VSR models are also similar to each other, so we propose to use "Ghost features" to reduce this redundancy. We also analyze the so-called "gradient disappearance" phenomenon generated by the conventional recurrent convolutional network (RNN) model, and combine the Ghost module with RNN to complete the modeling on time series. The current frame is used as input to the model together with the next frame, the output of the previous frame and the hidden state. Extensive experiments on several benchmark models and datasets show that the PSNR and SSIM of our proposed modality are improved to some extent. Some texture details in the video are also better preserved.
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