arXiv Computer Vision
Sep 10

SloMoDeblur: A Large-Scale Smartphone Image Deblurring Dataset

arXiv:2506.19445v5 Announce Type: replace Abstract: Motion blur remains one of the most common and visually disruptive degradations in real-world smartphone imaging, yet existing deblurring benchmark...

By Syed Mumtahin Mahmud, Mahdi Mohd Hossain Noki, Prothito Shovon Majumder, Abdul Mohaimen Al Radi, Sudipto Das Sukanto, Afia Lubaina, Md. Mosaddek Khan
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.

By Renbiao Jin, Mingxin Yang, Yutian Chen, Junhao Zhuang, Xin Cai, Mulin Yu, Linning Xu, Wenxian Yu, Danping Zou, Shi Guo, Tianfan Xue
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.

By Junseong Shin, Hyeonsu Jo, Daehyun Kim, Tae Hyun Kim
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.

By Xintian Mao, Jiansheng Wang, Huaiyuan Wang, Qingli Li, Yan Wang