Hugging Face Trending Papers
Controllable blind deblurring with diffusion models
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arXiv Computer Vision
Aug 25
Controllable blind deblurring with diffusion models
The paper introduces SuperSharpen, a diffusion-based method for blind deblurring in professional photography that can invert unknown isotropic blur without knowing the degradation kernel. It offers explicit control over restoration strength via a blur measure and compares two conditioning strategies: a ControlNet-style adapter on a frozen backbone and full finetuning of the diffusion prior. Experiments on synthetic and real-world blur show that finetuning yields higher fidelity with fewer hallucinated details, improving perceptual quality and controllable restoration strength.
arXiv AI
Oct 19
FlashReFocus: Interactive Image Refocusing in Seconds
arXiv:2610.23297v1 Announce Type: cross Abstract: Single-image refocusing changes the focal plane and depth of field of a photograph after capture. It is inherently interactive: users try many focal...
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
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 Computer Vision
6d ago
Taming Noise Initialization for Zero-shot Video Restoration with Image Diffusion Priors
The paper introduces a method for zero‑shot video restoration that improves upon prior approaches by using DDIM inversion on corrupted video frames to generate noise that preserves temporal correlations. An adaptive noise blending strategy is added to reduce degradation patterns while gradually moving toward Gaussian noise, and a measurement‑guided trajectory attention mechanism further enhances temporal consistency. Experiments on DAVIS, REDS4, and Set8 datasets show that this approach outperforms existing zero‑shot video restoration methods.
arXiv Machine Learning
Sep 3
Perceptually Regularized Diffusion Model for Image Super-Resolution
The paper introduces a perceptually regularized diffusion framework for image super‑resolution, adding perceptual‑loss based regularization to the standard diffusion training objective. This approach incorporates prior knowledge to improve training convergence and encourages the recovery of meaningful image features. Experiments on benchmark datasets show enhanced perceptual quality while maintaining competitive distortion metrics.