Super Resolution
Super-resolution (SR) aims to enhance the resolution of images or other data, improving detail and clarity from lower-resolution inputs. Current research focuses on developing efficient and effective SR models, employing various architectures such as convolutional neural networks, transformers, and diffusion models, often incorporating techniques like self-supervised learning and multi-scale processing to improve performance and reduce computational cost. These advancements have significant implications across diverse fields, including medical imaging (improving diagnostic accuracy), remote sensing (enhancing spatial detail), and computer vision (improving the quality of generated images and videos). The development of robust and efficient SR methods is crucial for numerous applications where high-resolution data is desirable but acquisition is costly or impractical.
Papers - Page 3
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting
Mojtaba Safari, Shansong Wang, Zach Eidex, Qiang Li, Erik H. Middlebrooks, David S. Yu, Xiaofeng YangEmory University●Mayo ClinicDifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution
Xingyuan Li, Zirui Wang, Yang Zou, Zhixin Chen, Jun Ma, Zhiying Jiang, Long Ma, Jinyuan LiuDalian University of Technology●Northwestern Polytechnical University●Waseda University●Dalian Maritime University
BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution
Xue Yang, Tao Chen, Lei Guo, Wenbo Jiang, Ji Guo, Yongming Li, Jiaming HeUniversity of Electronic Science and Technology of China●XinJiang University●Chengdu University of TechnologyInspireMusic: Integrating Super Resolution and Large Language Model for High-Fidelity Long-Form Music Generation
Chong Zhang, Yukun Ma, Qian Chen, Wen Wang, Shengkui Zhao, Zexu Pan, Hao Wang, Chongjia Ni, Trung Hieu Nguyen, Kun Zhou, Yidi Jiang+4Alibaba GroupDelta-WKV: A Novel Meta-in-Context Learner for MRI Super-Resolution
Rongchang Lu, Bingcheng Liao, Haowen Hou, Jiahang Lv, Xin HaiQinghai University●Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)MFSR-GAN: Multi-Frame Super-Resolution with Handheld Motion Modeling
Fadeel Sher Khan, Joshua Ebenezer, Hamid Sheikh, Seok-Jun LeeThe University of Texas at Austin●Samsung Research AmericaContinual Learning-Aided Super-Resolution Scheme for Channel Reconstruction and Generalization in OFDM Systems
Jianqiao Chen, Nan Ma, Wenkai Liu, Xiaodong Xu, Ping ZhangZGC Institute of Ubiquitous-X Innovation and Applications●Beijing University of Posts and Telecommunications
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution
Kai Liu, Dehui Wang, Zhiteng Li, Zheng Chen, Yong Guo, Wenbo Li, Linghe Kong, Yulun ZhangShanghai Jiao Tong University●South China University of Technology●Huawei Noah’s Ark LabSuper-Resolution for Interferometric Imaging: Model Comparisons and Performance Analysis
Hasan Berkay Abdioglu, Rana Gursoy, Yagmur Isik, Ibrahim Cem Balci, Taha Unal, Kerem Bayer, Mustafa Ismail Inal, Nehir Serin+3Yildiz Technical University●Marmara University
GVTNet: Graph Vision Transformer For Face Super-Resolution
Chao Yang, Yong Fan, Cheng Lu, Minghao Yuan, Zhijing YangSouthwest University of Science and TechnologyDeltaDiff: A Residual-Guided Diffusion Model for Enhanced Image Super-Resolution
Chao Yang, Yong Fan, Cheng Lu, Zhijing YangSouthwest University of Science and TechnologyMulti Image Super Resolution Modeling for Earth System Models
Ehsan Zeraatkar, Salah A Faroughi, Jelena TešićTexas State University●University of Utah
Per-channel autoregressive linear prediction padding in tiled CNN processing of 2D spatial data
Olli Niemitalo, Otto Rosenberg, Nathaniel Narra, Olli Koskela, Iivari Kunttu (HAMK Häme University of Applied Sciences)VoLUT: Efficient Volumetric streaming enhanced by LUT-based super-resolution
Chendong Wang, Anlan Zhang, Yifan Yang, Lili Qiu, Yuqing Yang, Xinyang Jiang, Feng Qian, Suman BanerjeeUniversity of Wisconsin–Madison●University of Southern California●Microsoft Research AsiaOn the Logic Elements Associated with Round-Off Errors and Gaussian Blur in Image Registration: A Simple Case of Commingling
Serap A. Savari