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

• URL: http://arxiv.org/abs/2309.00956v1
• Date: Sat, 2 Sep 2023 14:50:13 GMT
• Title: ASF-Net: Robust Video Deraining via Temporal Alignment and Online Adaptive Learning
• Authors: Xinwei Xue, Jia He, Long Ma, Xiangyu Meng, Wenlin Li, Risheng Liu
• Abstract summary: We propose a new computational paradigm, Alignment-Shift-Fusion Network (ASF-Net), which incorporates a temporal shift module. We construct a LArge-scale RAiny video dataset (LARA) which supports the development of this community. Our proposed approach exhibits superior performance in three benchmarks and compelling visual quality in real-world scenarios.
• Score: 47.10392889695035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://github.com/vis-opt-group/ASF-Net.

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