.png){kind=icon}
.png){kind=icon}
Return
FaverNet: All-in-One Video Restoration via Frequency-Discriminative Conditioning
.png){kind=icon}
PRE
AI
H
Y
B
X
W
X
DOI：10.1007/s11263-026-02977-y.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Traditional video restoration methods are typically designed for a single, known degradation type, while real-world videos often experience time-varying and unknown degradations. Although more general restoration methods have been proposed, they largely overlook the time-varying nature of real-world degradations. In this work, we focus on time-varying unknown degradations, which introduce frame-wise distortions and pose two significant challenges to video restoration. First, the unknown nature of degradations expands the input degradation space and introduces significant uncertainty into the restoration process. Second, time-varying degradations cause inconsistent distortions across frames, hindering the effective aggregation of temporal information from neighboring frames. To address these two challenges, we propose FaverNet, a Frequency-guided All-in-one VidEo Restoration Network, which incorporates a Frequency-discriminative Conditioning Mechanism (FCM) and a Prompt-guided Alignment Mechanism (PAM). Specifically, FCM enhances the degradation-awareness of the restoration process by conditioning the model with degradation cues extracted from frequency domain, where different degradation types are more separable than in the spatial domain. PAM further leverages these degradation cues to assist alignment between neighboring frames, enabling temporally coherent and degradation-robust information aggregation. Furthermore, we build two benchmarks covering both classic low-level distortions (e.g., noise, blur) and complex weather-related degradations (e.g., haze, rain). Experiments show that FaverNet offers an encouraging solution for practical video restoration and achieves superior performance.
Keywords:
Video restoration
All-in-one video restoration
Time-varying unknown degradations
Adverse weather conditions
Journal
IF:
9.3
Papers:
3.9K
Citations:
2.8W
Organization
Cited Papers