BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

A recurrent structure is a popular framework choice for the task of video super-resolution. The state-of-the-art method BasicVSR adopts bidirectional propagation with feature alignment to effectively exploit information from the entire input video. In this study, we redesign BasicVSR by proposing second-order grid propagation and flow-guided deformable alignment. We show that by empowering the recurrent framework with the enhanced propagation and alignment, one can exploit spatiotemporal information across misaligned video frames more effectively. The new components lead to an improved perform…

On the Generalization of BasicVSR++ to Video Deblurring and Denoising

The exploitation of long-term information has been a long-standing problem in video restoration. The recent BasicVSR and BasicVSR++ have shown remarkable performance in video super-resolution through long-term propagation and effective alignment. Their success has led to a question of whether they can be transferred to different video restoration tasks. In this work, we extend BasicVSR++ to a generic framework for video restoration tasks. In tasks where inputs and outputs possess identical spatial size, the input resolution is reduced by strided convolutions to maintain efficiency. With only m…

Mitigating Artifacts in Real-World Video Super-Resolution Models

The recurrent structure is a prevalent framework for the task of video super-resolution, which models the temporal dependency between frames via hidden states. When applied to real-world scenarios with unknown and complex degradations, hidden states tend to contain unpleasant artifacts and propagate them to restored frames. In this circumstance, our analyses show that such artifacts can be largely alleviated when the hidden state is replaced with a cleaner counterpart. Based on the observations, we propose a Hidden State Attention (HSA) module to mitigate artifacts in real-world video super-re…

用于高效视频超分辨率的级联时间更新网络

Cascaded Temporal Updating Network for Efficient Video Super-Resolution

提出级联时间更新网络(CTUN)，通过隐式级联对齐模块和利用未来信息的单向传播更新器，在保持性能的同时大幅减少参数和推理时间，实现高效视频超分辨率。

SAM能否提升视频超分辨率？

Can SAM Boost Video Super-Resolution?

本文提出利用分割一切模型（SAM）作为语义感知先验，通过轻量级模块SEEM增强视频超分辨率中的对齐与融合，在EDVR和BasicVSR上于三个数据集上持续提升性能。

Essential Tensor Learning for Multi-view Spectral Clustering

Multi-view clustering attracts much attention recently, which aims to take advantage of multi-view information to improve the performance of clustering. However, most recent work mainly focus on self-representation based subspace clustering, which is of high computation complexity. In this paper, we focus on the Markov chain based spectral clustering method and propose a novel essential tensor learning method to explore the high order correlations for multi-view representation. We first construct a tensor based on multi-view transition probability matrices of the Markov chain. By incorporating…