arXiv:2506. 19445v4 Announce Type: cross Abstract: We introduce the largest real-world image deblurring dataset constructed from smartphone slow-motion videos.
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...
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: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: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.
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.
arXiv:2609.27274v1 Announce Type: new Abstract: Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow...
arXiv:2509.24644v5 Announce Type: replace Abstract: Capturing screens is now routine in our everyday lives. But the photographs of emissive displays are often influenced by the flicker-banding (FB),...
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:2609.07012v1 Announce Type: cross Abstract: Pushbroom satellite imaging couples limited spatial resolution with platform attitude instability. Platform jitter produces spatially varying motion...
Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow detail loss, making high-quality high dynamic ra...
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.