arXiv Computer Vision
SloMoDeblur: A Large-Scale Smartphone Image Deblurring Dataset
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arXiv AI
Jul 24
Deblurring in the Wild: A Real-World Image Deblurring Dataset from Smartphone High-Speed Videos
arXiv:2506. 19445v4 Announce Type: cross Abstract: We introduce the largest real-world image deblurring dataset constructed from smartphone slow-motion videos.
arXiv Computer Vision
Oct 7
TSRN-RTVD: Real-Time Video Deblurring System
arXiv:2610.08230v1 Announce Type: new Abstract: As video capture moves to handheld and edge devices, motion blur from camera shake has become a pervasive degradation that lowers perceptual quality an...
arXiv AI
Oct 2
PickMoment: Continuous-Time Single-Image-to-Video via Learning Deblurring and Blur-to-Video
PickMoment is a continuous‑time model that learns to predict the interval‑mean blur over arbitrary sub‑intervals of a camera exposure, unifying single‑image deblurring, blur‑to‑video generation, and continuous‑time pick‑a‑moment recovery. It is trained with three supervisions derived from the blur integral: an empirical reconstruction loss, an additivity loss for self‑consistency, and a sharp‑frame loss at zero interval. The model achieves state‑of‑the‑art performance on GoPro and HIDE for generative deblurring, competitive results on RealBlur, and the highest per‑frame fidelity on GoPro‑7 blur‑to‑video, all in a single forward pass.
arXiv AI
Jul 24
RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring
arXiv:2607. 20628v1 Announce Type: cross Abstract: Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic training data, yet robust restoration is critical for downstream pipelines such as mobile imaging and 3D reconstruction.
arXiv AI
Jul 1
FMA-Net++: Motion- and Exposure-Aware Joint Video Super-Resolution and Deblurring
arXiv:2512. 04390v2 Announce Type: replace-cross Abstract: Joint video super-resolution and deblurring (VSRDB) requires both efficient long-range temporal modeling and robustness to frame-wise exposure-duration variation, which changes the extent of motion blur across video frames.
arXiv Computer Vision
Oct 12
UniDeblur: Unified Motion Deblurring via Fourier Convolution Learning
UniDeblur introduces a unified motion deblurring framework that learns blur-adaptive global filters in Fourier space, addressing the limitations of pixel-level mapping in existing methods. By constructing a multi-source dataset and implementing a data pruning and quality control pipeline, the model can handle diverse motion blur kernels across different cameras and image contents. Experiments show that UniDeblur performs competitively on both in-domain and out-of-domain benchmarks, demonstrating its effectiveness for varied blur scenarios.