ASF-Net: Robust Video Deraining via Temporal Alignment and Online Adaptive Learning

by   Xinwei Xue, et al.

In recent times, learning-based methods for video deraining have demonstrated commendable results. However, there are two critical challenges that these methods are yet to address: exploiting temporal correlations among adjacent frames and ensuring adaptability to unknown real-world scenarios. To overcome these challenges, we explore video deraining from a paradigm design perspective to learning strategy construction. Specifically, we propose a new computational paradigm, Alignment-Shift-Fusion Network (ASF-Net), which incorporates a temporal shift module. This module is novel to this field and provides deeper exploration of temporal information by facilitating the exchange of channel-level information within the feature space. To fully discharge the model's characterization capability, we further construct a LArge-scale RAiny video dataset (LARA) which also supports the development of this community. On the basis of the newly-constructed dataset, we explore the parameters learning process by developing an innovative re-degraded learning strategy. This strategy bridges the gap between synthetic and real-world scenes, resulting in stronger scene adaptability. Our proposed approach exhibits superior performance in three benchmarks and compelling visual quality in real-world scenarios, underscoring its efficacy. The code is available at


Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior

Video dehazing aims to recover haze-free frames with high visibility and...

Order Matters: Shuffling Sequence Generation for Video Prediction

Predicting future frames in natural video sequences is a new challenge t...

TartanVO: A Generalizable Learning-based VO

We present the first learning-based visual odometry (VO) model, which ge...

Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring

The success of the state-of-the-art video deblurring methods stems mainl...

Towards Real-World Visual Tracking with Temporal Contexts

Visual tracking has made significant improvements in the past few decade...

TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device

The explosive growth in video streaming requires video understanding at ...

Temporal Shift Module for Efficient Video Understanding

The explosive growth in online video streaming gives rise to challenges ...

Please sign up or login with your details

Forgot password? Click here to reset