Semi-Supervised State-Space Model with Dynamic Stacking Filter for Real-World Video Deraining

TL;DR

This paper introduces a semi-supervised, dual-branch state-space model with a dynamic stacking filter for improved real-world video deraining, enhancing generalization and supporting downstream vision tasks.

Contribution

It proposes a novel dual-branch spatio-temporal state-space model with a dynamic stacking filter and median stacking loss for semi-supervised video deraining in real-world scenarios.

Findings

Outperforms existing methods on synthetic and real-world benchmarks

Improves object detection and tracking in rainy conditions

Demonstrates efficiency and superior visual quality

Abstract

Significant progress has been made in video restoration under rainy conditions over the past decade, largely propelled by advancements in deep learning. Nevertheless, existing methods that depend on paired data struggle to generalize effectively to real-world scenarios, primarily due to the disparity between synthetic and authentic rain effects. To address these limitations, we propose a dual-branch spatio-temporal state-space model to enhance rain streak removal in video sequences. Specifically, we design spatial and temporal state-space model layers to extract spatial features and incorporate temporal dependencies across frames, respectively. To improve multi-frame feature fusion, we derive a dynamic stacking filter, which adaptively approximates statistical filters for superior pixel-wise feature refinement. Moreover, we develop a median stacking loss to enable semi-supervised learning by generating pseudo-clean patches based on the sparsity prior of rain. To further explore the capacity of deraining models in supporting other vision-based tasks in rainy environments, we introduce a novel real-world benchmark focused on object detection and tracking in rainy conditions. Our method is extensively evaluated across multiple benchmarks containing numerous synthetic and real-world rainy videos, consistently demonstrating its superiority in quantitative metrics, visual quality, efficiency, and its utility for downstream tasks.