MIAR introduces a multi‑scale autoregressive framework for medical image super‑resolution, treating the task as a conditional, progressive next‑scale prediction. It incorporates a Scale‑Adaptive Structural Decoder to preserve structural fidelity and uses a hierarchical beam search during inference to reduce recursive error accumulation. Experiments show MIAR outperforms existing methods, achieving a 7.86% MUSIQ improvement and a 2.02× speedup over diffusion‑based approaches.
By Fang Li, Yinglong Li, Hongyu Wu, Yang Gao, Minwei Zhao, Aimin Hao
arXiv:2608. 14744v1 Announce Type: new Abstract: Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation.
By Mrigank Dhingra, Ramchandran Muthukumar, Rebecca Willett, Omer San
arXiv:2608. 10544v1 Announce Type: cross Abstract: Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations.
By Sangwoo Jo, Donggeun Ko, Jayeon Kang, Youngsang Kwak, Jaehwa Kwak, Sungjoon Choi
arXiv:2507. 00719v3 Announce Type: replace-cross Abstract: Typically, numerical simulations of Earth systems are coarse, and Earth observations are sparse and gappy.
By Anantha Narayanan Suresh Babu, Akhil Sadam, Pierre F. J. Lermusiaux
SelfLift is a progressive‑resolution framework that accelerates few‑step diffusion models by enabling late, self‑recovering transitions between low‑ and high‑resolution latents. It introduces a training‑free Artifact‑Aware Consistency Lift that uses disagreement between direct latent lifting and pixel‑VAE re‑encoding to detect and correct artifacts, and a self‑recovery policy that transfers high‑resolution guidance from an internal teacher. Experiments on FLUX.2‑Klein and Z‑Image‑Turbo show latency reductions of 41.5% and 44.1%, and overall speedups of 29.61× and 19.21× over 50‑step baselines while maintaining competitive generation quality.
By Tingyan Wen, Chenqian Yan, Xurui Peng, Xiazhang Fang, Shuai Wang, Xueqian Wang, Songwei Liu
The paper introduces a high‑resolution Rectified Flow Matching model trained on 256×256 chest CT images to serve as a generative prior for CT reconstruction. A two‑stage training strategy—initial strong anatomically informed augmentation followed by fine‑tuning—helps mitigate overfitting and improve structural fidelity. When evaluated on various CT inverse problems, Flow Matching‑based reconstruction methods outperform diffusion‑based algorithms in PSNR, SSIM, and perceptual quality while requiring fewer sampling steps.
By Davide Evangelista
The paper introduces MS-Flow, a method that represents a flow-based generative model’s trajectory as a sequence of intermediate latent states instead of a single initial code. By enforcing local flow dynamics and coupling trajectory segments with matching penalties, the approach alternates between updating latent states and ensuring consistency with observed data. This strategy reduces memory usage and improves reconstruction quality on tasks such as image inpainting, super‑resolution, and computed tomography.
By Alexander Denker, Zeljko Kereta, Carola-Bibiane Sch\"onlieb, Moshe Eliasof
arXiv:2607. 12433v1 Announce Type: cross Abstract: Diffusion models have recently become the dominant paradigm for monocular depth estimation (MDE).
By Zijie Wang, Wei Zhang, Weiming Zhang, Xiao Tan, Weikai Chen, Xiaoxu Li, Guanbin Li
arXiv:2609.23404v1 Announce Type: new
Abstract: Point cloud completion aims to infer a complete 3D shape from a partial point cloud and serves as a fundamental building block for downstream tasks suc...
By Shenghui Wu, Chen Wang, Yuan Feng, Guangshun Wei, Yuanfeng Zhou, Changjian Li
arXiv:2607. 15711v1 Announce Type: cross Abstract: Diffusion-based methods have achieved impressive performance in real-world image super-resolution (Real-ISR) by leveraging large pre-trained stable diffusion (SD) models as powerful generative priors.
By Xue Wu, Kang Zhao, Kafeng Wang, Jianfei Chen, Jingwei Xin, Nannan Wang, Xinbo Gao
arXiv:2603.20186v2 Announce Type: replace
Abstract: In this work, we propose Image-to-Image Rectified Flow Reformulation (I2I-RFR), a practical plug-in reformulation that recasts standard I2I regress...
By Satoshi Iizuka, Shun Okamoto, Kazuhiro Fukui
arXiv:2607. 10140v1 Announce Type: cross Abstract: Existing optical flow methods broadly follow two paradigms: iterative optimization and diffusion-based estimation.
By Yuang Meng, Chenyang Wu, Xianshun Liu, Chun-Le Guo, Zichen Liang, Lina Lei, Jie Liang, Hui Zeng, Chongyi Li, Lei Zhang