Image Super-Resolution

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Flow-based Gaussian Splatting for Continuous-Scale Remote Sensing Image Super-Resolution
May 21, 2026Jiangwei Mo, Xi Lu, Hanlin WuRemote Sensing Image Super-Resolution Image Super-Resolution
School of Information Science and Technology, Beijing Foreign Studies University, Beijing 100089, China
Multi-scale interaction network for stereo image super-resolution
May 21, 2026Liyi Xu, Lin QiImage Super-Resolution
Ocean University of China, Qingdao, 266100, China
SR-Ground: Image Quality Grounding for Super-Resolved Content
May 20, 2026Artem Borisov, Evgeney Bogatyrev, Khaled Abud +1Image Quality Assessment Image Super-Resolution
Lomonosov Moscow State University Moscow, Russia · MSU AI Center, Lomonosov Moscow State University Moscow, Russia
GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance
May 18, 2026Jiale Shi, Jiarui Hu, Zesong Yang +33D Gaussian Splatting 3D Reconstruction
State Key Lab of CAD & CG, Zhejiang University
Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution
May 18, 2026Bin Luo, Runmin Dong, Zhaoyang Luo +4Diffusion Models for Image Restoration Diffusion Transformer
Tsinghua Shenzhen International Graduate School, Shenzhen, China · Sun Yat-sen University, Zhuhai, China · Tsinghua University, Beijing, China +1
MSIQ: Moment-based Scale-Invariant Quality Measure for Single Image Super-Resolution
May 17, 2026Leonid BedratyukImage Quality Assessment Image Super-Resolution
EchoSR: Efficient Context Harnessing for Lightweight Image Super-Resolution
May 17, 2026Hanli Zhao, Binhao Wang, Shihao Zhao +3Multi-Scale Feature Fusion Image Super-Resolution
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325000, China · vivo BlueImage Lab, vivo Mobile Communication Co., Ltd, Shanghai 200100, China · College of Engineering and Computer Science, Australian National University, Canberra, Australia +1
DreamSR: Towards Ultra-High-Resolution Image Super-Resolution via a Receptive-Field Enhanced Diffusion Transformer
May 15, 2026Qingji Dong, Hang Dong, Mingqin Chen +2Diffusion Models for Image Restoration Diffusion Model Guidance
ByteDance Inc.
Hierarchical Image Tokenization for Multi-Scale Image Super Resolution
May 14, 2026Isma Hadji, Enrique Sanchez, Adrian Bulat +2Image Tokenization Visual Autoregressive Models
Samsung AI Center Cambridge, UK · Technical University of Iasi, Romania · Queen Mary University of London, UK
SR-Prominence: A Crowdsourced Protocol and Dataset Suite for Perceptually-Weighted Super-Resolution Artifact Evaluation
May 14, 2026Ivan Molodetskikh, Kirill Malyshev, Mark Mirgaleev +3Image Quality Assessment Image Super-Resolution
AI Center, Lomonosov Moscow State University GSP-1, Leninskie Gory, Moscow, 119991, Russia · Lomonosov Moscow State University · Innopolis University Universitetskaya St, 1, Innopolis, Respublika Tatarstan, 420500, Russia +1
You Only Landmark Once: Lightweight U-Net Face Super Resolution with YOLO-World Landmark Heatmaps
May 13, 2026Riccardo Carraro, Anna Briotto, Endi Hysa +2Image Super-Resolution
University of Padua, Padua, Italy
OP4KSR: One-Step Patch-Free 4K Super-Resolution with Periodic Artifact Suppression
May 13, 2026Chengyan Deng, Pengbin Yu, Zhentao Chen +6Diffusion Transformer Diffusion Model Inference Acceleration
School of Automation Engineering, University of Electronic Science and Technology of China · OPPO AI Center, OPPO Inc. · School of Intelligence Science and Technology, Nanjing University
DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution
May 13, 2026Zheng Chen, Ruofan Yang, Jin Han +5Video Diffusion Models Video Super-Resolution
Shanghai Jiao Tong University · Huawei Noah’s Ark Lab · Duke University +1
PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution
May 13, 2026Zihang Xu, Xiaoyang Liu, Zheng Chen +2Diffusion Models for Image Restoration Image Super-Resolution
Shanghai Jiao Tong University
Fast Image Super-Resolution via Consistency Rectified Flow
May 12, 2026Jiaqi Xu, Wenbo Li, Haoze Sun +8Rectified Flow Consistency Distillation
The Chinese University of Hong Kong · Huawei Noah’s Ark Lab · HKUST (GZ) +1
3DGS $^3$ : Joint Super Sampling and Frame Interpolation for Real-Time Large-Scale 3DGS Rendering
May 12, 2026Yibo Zhao, Fan Gao, Youcheng Cai +13D Gaussian Splatting Video Frame Interpolation
University of Science and Technology of China
Adaptive Context Matters: Towards Provable Multi-Modality Guidance for Super-Resolution
May 11, 2026Jinyi Luo, Minghao Liu, Yifan Li +2Mixture of Experts Multimodal Fusion
Wangxuan Institute of Computer Technology, Peking University
ExtraVAR: Stage-Aware RoPE Remapping for Resolution Extrapolation in Visual Autoregressive Models
May 11, 2026Feihong Yan, Shaoyu Liu, Haixuan Wang +4Rotary Positional Embeddings Visual Autoregressive Models
Beijing Institute of Technology · Xidian University · Northeastern University at Qinhuangdao +3
Spatial-Frequency Gated Swin Transformer for Remote Sensing Single-Image Super-Resolution
May 10, 2026Md Aminur Hossain, Parekh Valkesh, Ayush V. Patel +3Remote Sensing Image Understanding Vision Transformer
Space Applications Centre, ISRO, Ahmedabad, India · New L J Institute of Engineering and Technology, Ahmedabad, India · Pandit Deendayal Energy University, Gandhinagar, India +2
DegBins: Degradation-Driven Binning for Depth Super-Resolution
May 10, 2026Zhiqiang Yan, Zhengxue Wang, Jian Yang +1Depth Estimation Residual Learning
Department of Computer Science, National University of Singapore · Nanjing University of Science and Technology
Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement
May 10, 2026Wei Zhu, Kai Zhang, Yu Zheng +3Flow Matching Diffusion Model Distillation
Nanjing University of Science and Technology · Nanjing University · Huawei
Supersampling Stable Diffusion and Beyond: A Seamless, Training-Free Approach for Scaling Neural Networks Using Common Interpolation Methods
May 9, 2026Md Abu Obaida Zishan, Jannatun Noor, Annajiat Alim RaselLatent Diffusion Models Diffusion-Based Image Generation
School of Data and Sciences BRAC University, Dhaka · Computing for Sustainability and Social Good (C2SG) Research Group Department of Computer Science and Engineering United Internation University, Dhaka
MicroDiffuse3D: A Foundation Model for 3D Microscopy Imaging Restoration
May 8, 2026Yongkang Li, Brian Wong, King Wai Chiu +5Image Restoration Microscopy Image Analysis
Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA. · Department of Chemistry, University of Washington, Seattle, WA, USA
Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework
May 8, 2026Linxiao Shi, Siming Zheng, Zerong Wang +5Neural Rendering Image Super-Resolution
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · vivo BlueImage Lab, vivo Mobile Communication Co., Ltd. · Shenzhen University of Advanced Technology
GTF: Omnidirectional EPI Transformer for Light Field Super-Resolution
May 6, 2026Kunyu Li, Fei Wang, Lichao Zhang +2Image Super-Resolution
School of Computer Science and Technology, Xi’an Shiyou University · School of Electronic Engineering, Xi’an Shiyou University · Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology
FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution
May 5, 2026Shuhong Liu, Xining Ge, Ziteng Cui +8Flow Matching Image Super-Resolution
1The University of Tokyo · 2I2WM · 3Tohoku University +1
CASISR: Circular Arbitrary-Scale Image Super-Resolution
May 5, 2026Honggui Li, Zhengyang Zhang, Dingtai Li +11Image Super-Resolution
School of Information and Artificial Intelligence, Yangzhou University, Yangzhou 225127, China · Shanghai Qigong Research Institute, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China · LISITE Research Laboratory, Institut Supérieur d'Électronique de Paris, Paris 75006, France +4
TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution
May 4, 2026Sowmya Vajrala, Akshay Bankar, Manjunath Arveti +5Diffusion Distillation Few-Step Diffusion Sampling
Samsung Research Institute Bangalore, India
Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation
May 4, 2026Mete Ahishali, Anis Ur Rahman, Einari Heinaro +2Remote Sensing Image Segmentation Unsupervised Domain Adaptation
Department of Forest Sciences, University of Helsinki, Finland · CSC – IT Center for Science Ltd., Espoo, Finland · KOKO Forest Ltd. +1
SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation
May 4, 2026Ce Wang, Zhenyu Hu, Wanjie SunDiffusion Model Distillation Remote Sensing Image Super-Resolution
School of Remote Sensing and Information Engineering, Wuhan University; Wuhan 430079, China

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Flow-based Gaussian Splatting for Continuous-Scale Remote Sensing Image Super-Resolution

Jiangwei Mo, Xi Lu, Hanlin Wu

School of Information Science and Technology, Beijing Foreign Studies University, Beijing 100089, China

Remote Sensing Image Super-Resolution Image Super-Resolution

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High-resolution remote sensing images (RSIs) are crucial for Earth observation applications, yet acquiring them is often limited by sensor constraints and costs. In recent years, generative super-resolution (SR) methods, particularly diffusion models, have made significant progress. However, they typically require slow iterative inference with 40--1000 steps and exhibit limited flexibility in continuous-scale SR settings. To address these issues, we propose FlowGS, a generative reconstruction framework for arbitrary-scale SR of RSIs. FlowGS models the high-frequency detail representations between high- and low-resolution images and learns a continuous probability flow from noise to detail priors via flow matching (FM) constrained by shortcut consistency, thereby reducing generative complexity and improving inference efficiency. Additionally, we employ 2D Gaussian splatting to construct a continuous feature field, thereby enabling flexible reconstruction at arbitrary query locations. Experimental results show that FlowGS delivers competitive perceptual quality compared with existing methods in both continuous-scale and fixed-scale SR settings, with substantially improved inference efficiency.

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