Moment-Reenacting: Inverse Motion Degradation with Cross-shutter Guidance

2026-05-21

AI Summary

This work addresses the challenges of motion blur in global shutter (GS) images and geometric distortion in rolling shutter (RS) images caused by fast motion or low-light conditions, proposing the first unified restoration framework. By leveraging synchronously captured blurry-GS/distorted-RS image pairs, the method jointly models their complementary degradation characteristics to inversely reconstruct the underlying high-speed scene. Key innovations include a novel dual-shutter stereo Blur-RS hardware setup for acquiring real-world paired data, and a dual-stream motion representation disentanglement module coupled with a self-prompted frame reconstruction network, enabling synergistic optimization of motion parsing and image restoration. Experiments demonstrate that the approach significantly improves reconstruction quality and generalization under complex motion degradations, establishing a new paradigm for high-speed video restoration in real-world scenarios.

Abstract

Motion degradation, manifested as blur in global shutter (GS) images or rolling shutter (RS) distortion in RS counterparts, remains a fundamental challenge in computational imaging, especially under fast motion or low-light conditions. While prior works have treated blur decomposition and RS temporal super-resolution as separate tasks, this separation fails to exploit their intrinsic complementarity. In this paper, we propose a unified framework to invert motion degradation and reenact imaging moment by jointly leveraging the complementary characteristics of GS blur and RS distortion. To this end, we introduce a novel dual-shutter setup that captures synchronized blur-RS image pairs and demonstrate that this combination effectively resolves temporal and spatial ambiguities inherent in both modalities. For allowing flexible performance-cost trade-offs, we further extend this dual-shutter setup to a stereo Blur-RS configuration with a narrow baseline. In addition, we construct a triaxial imaging system to collect a real-world dataset with aligned GS-RS pairs and ground-truth high-speed frames, enabling robust training and evaluation beyond synthetic data. Our proposed network explicitly disentangles motion into context-aware and temporally-sensitive representations via a dual-stream motion interpretation module, followed by a self-prompted frame reconstruction stage. Extensive experiments validate the superiority and generalizability of our approach, establishing a new paradigm for realistic high-speed video reconstruction under complex motion degradations. Codes and more resources are available at https://jixiang2016.github.io/dualBR_site/.

Problem

• motion degradation
• global shutter blur
• rolling shutter distortion
• computational imaging
• temporal super-resolution

Innovation

• dual-shutter imaging
• motion degradation inversion
• rolling shutter distortion
• temporal super-resolution
• real-world dataset