ASTRA (Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignment) tackles the challenge of reconstructing dynamic 3D scenes from temporally asynchronous multi‑camera data. By using 2D motion trajectories as texture‑robust supervision, it jointly optimizes temporal offsets and 3D representations, aligning projected 3D point motion with observed 2D paths while masking unreliable constraints. Experiments on Gaussian Splatting backbones show that ASTRA retains high‑frequency spatial detail, improves PSNR by ~1.4 dB, reduces temporal‑offset MAE by 54 %, and nearly quadruples synchronization success even with up to 25‑frame offsets.
By Junyu Zhu, Hao Zhu, Xinzhuo Zhang, Xu Zhang, Hongdong Li, Zhan Ma, Xun Cao
arXiv:2602.19736v3 Announce Type: replace
Abstract: Diffusion models now give the best perceptual quality in super-resolution (SR), but their architecture and training confine them to small fixed cro...
By Shoukun Sun, Zhe Wang, Xiang Que, Jiyin Zhang, Xiaogang Ma
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:2505. 06668v2 Announce Type: replace-cross Abstract: We present StableMotion, a novel framework that leverages geometric and content priors from pretrained large-scale image diffusion models for motion estimation in single-image rectification tasks such as Stitched Image Rectangling (SIR) and Rolling Shutter Correction (RSC).
By Ziyi Wang, Haipeng Li, Lin Sui, Tianhao Zhou, Hai Jiang, Lang Nie, Bing Zeng, Shuaicheng Liu
The paper introduces LEADer, a framework that uses local epistemic uncertainty to guide active sampling in diffusion-based image restoration. By adjusting prior strength per pixel and pruning sampling trajectories based on uncertainty traces, LEADer balances detail preservation with artifact suppression and accelerates convergence. The method is plug‑and‑play, theoretically guarantees data consistency and stable convergence, and improves performance across multiple state‑of‑the‑art diffusion models with minimal memory overhead.
By Jiaqi Zhang, Zheng Pang, Rongrong Gao, Qiyuan Zhang, Yang Yang
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:2604.24136v3 Announce Type: replace
Abstract: Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohi...
By Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu, Chia-Hung Yuan, Wei-Chen Chiu, Ching-Chun Huang
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.
By Geunhyuk Youk, Jihyong Oh, Munchurl Kim
arXiv:2606. 19802v1 Announce Type: new Abstract: Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithful images.
By Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
arXiv:2608.30782v1 Announce Type: new
Abstract: Real-world image super-resolution (Real-ISR) aims to preserve structures supported by the degraded observation while reconstructing perceptually realis...
By Bingtian Qiao, Yue Shi, Yong Guo, Wenjun Zhang, Jiezhang Cao
arXiv:2607. 14894v1 Announce Type: cross Abstract: Plug-and-play proximal gradient descent (PnP-PGD) enables flexible image reconstruction by using denoisers as implicit priors.
By Guixian Xu, Jinglai Li, Junqi Tang
Manifold4D introduces a new denoising strategy for video re‑shooting that injects a rendered point‑cloud directly into the initial noise manifold, eliminating the need for the render to be an explicit conditioning stream during denoising. This approach allows the network to rely solely on the source video as a visual condition, improving camera‑control accuracy on the DAVIS‑Traj benchmark and Vista4D set, with significant reductions in rotation and translation errors while maintaining video fidelity. User studies confirm enhanced trajectory following and dynamic consistency, especially for large yaw amplitudes and even when the render is corrupted.
By Yongqi Mao, Zijia Dai, Zhishuo Liu, Wei Xu, Kaiwei Wang, Guotao Meng