الملخص
Recently, unified high-definition image restoration has attracted considerable attention; however, existing models tend to excessively increase their depth in pursuit of improved generalization, which often yields only limited gains. Meanwhile, loop-based learning paradigms have drawn widespread attention due to their low parameter counts and strong regression capability, as exemplified by GPT-6 and looped Transformers. In this paper, we introduce the loop learning paradigm to address restoration tasks that require cross-domain learning. Specifically, we propose LoopMoEVR, a loop-based mixture-of-experts model capable of handling degraded ultra-high-definition (UHD) inputs. First, a degradation-conditioned low-rank loop embedding is designed to construct input-dependent stage conditions. Second, a spatio-temporal iterative adaptive normalization module, termed IterAda3DN, is developed to fuse local features with global loop context, thereby performing position-wise affine modulation. Finally, the expert branches further integrate the attention-updated local and global video states with the loop conditions to generate dedicated modulation parameters, while an input-conditioned depth predictor adaptively configures the number of loop iterations. With only approximately 0.884M trainable parameters, the proposed model uniformly handles UHD video dehazing, deraining, denoising, and low-light enhancement tasks, achieving state-of-the-art restoration performance on both public benchmarks and real-world scenarios.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Xin, Y., Bai, R., Wang, Y., Gao, G., Liu, J., Lu, D., Fan, L., & Zheng, Z. (2026). LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration. https://omanscience.com/ar/articles/loopmoevr-loop-based-degradation-aware-mixture-of-experts-for-unified-uhd-video-restoration
MLA 9
Xin, Yucheng, et al. "LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration." https://omanscience.com/ar/articles/loopmoevr-loop-based-degradation-aware-mixture-of-experts-for-unified-uhd-video-restoration.
شيكاغو (المؤلف–التاريخ)
Xin, Yucheng, Runci Bai, Yongcong Wang, Guangwei Gao, Jiao Liu, Dianjie Lu, Linwei Fan, and Zhuoran Zheng. 2026. "LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration." https://omanscience.com/ar/articles/loopmoevr-loop-based-degradation-aware-mixture-of-experts-for-unified-uhd-video-restoration.
هارفارد
Xin, Y., Bai, R., Wang, Y., Gao, G., Liu, J., Lu, D., Fan, L. and Zheng, Z. (2026) 'LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration', Available at: https://omanscience.com/ar/articles/loopmoevr-loop-based-degradation-aware-mixture-of-experts-for-unified-uhd-video-restoration.
فانكوفر
Xin Y, Bai R, Wang Y, Gao G, Liu J, Lu D, et al. LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration. https://omanscience.com/ar/articles/loopmoevr-loop-based-degradation-aware-mixture-of-experts-for-unified-uhd-video-restoration
IEEE
Y. Xin, R. Bai, Y. Wang, G. Gao, J. Liu, D. Lu, L. Fan, and Z. Zheng, "LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration," https://omanscience.com/ar/articles/loopmoevr-loop-based-degradation-aware-mixture-of-experts-for-unified-uhd-video-restoration.