Image Super-Resolution is a fundamental problem in computer vision with broad applications spacing from medical imaging to satellite analysis. The ability to reconstruct high-resolution images from low-resolution inputs is crucial for enhancing downstream tasks such as object detection and segmentation. While deep learning has significantly advanced SR, achieving high-quality reconstructions with fine-grained details and realistic textures remains challenging, particularly at high upscaling factors. Recent approaches leveraging diffusion models have demonstrated promising results, yet they often struggle to balance perceptual quality with structural fidelity. In this work, we introduce ResQu a novel SR framework that integrates a quaternion wavelet preprocessing framework with latent diffusion models, incorporating a new quaternion wavelet- and time-aware encoder. Unlike prior methods that simply apply wavelet transforms within diffusion models, our approach enhances the conditioning process by exploiting quaternion wavelet embeddings, which are dynamically integrated at different stages of denoising. Furthermore, we also leverage the generative priors of foundation models such as Stable Diffusion. Extensive experiments on domain-specific datasets demonstrate that our method achieves outstanding SR results, outperforming in many cases existing approaches in perceptual quality and standard evaluation metrics. The code will be available after the revision process.
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Bosch Center for Artificial Intelligence（博世人工智能中心）；CISPA Helmholtz Center for Information Security（亥姆霍兹信息安全中心）
Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. While previous works have leveraged conditional generative models like GANs and VAEs for learning the likely future trajectories, accurately modeling the dependency structure of these multimodal distributions, particularly over long time horizons remains challenging. Normalizing flow based generative models can model complex distributions admitting exact inference. These include variants with split coupling invertible transformations that are easier to parallelize compared to their autore…
WaveDM: Wavelet-Based Diffusion Models for Image Restoration
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）
3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes
Chair for Machine Learning University of Mannheim（机器学习主任办公室曼海姆大学）；Autodesk AI Lab（Autodesk人工智能实验室）；MPI for Informatics Saarland Informatics Campus（信息学研究所萨尔兰州信息学校区）
3D Wavelet-Based Structural Priors for Controlled Diffusion in Whole-Body Low-Dose PET Denoising
School of Engineering, Zurich University of Applied Sciences, CH；Bioengineering Department；Imperial-X, Imperial College London, UK；DAMTP, University of Cambridge, UK；Lucerne University Teaching；Research Hospital, CH；Lung Institute, Imperial College London, UK；Cardiovascular Research Centre, Royal Brompton Hospital, UK；School of Biomedical Engineering \& Imaging Sciences, King's College London, UK；Yau Mathematical Sciences Center, Tsinghua University, CN
Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
School of Information Technology, Monash University Malaysia（墨尔本大学马来西亚分校信息科技学院）；School of Engineering, Monash University Malaysia（墨尔本大学马来西亚分校工程学院）；Statistics Program, King Abdullah University of Science and Technology（国王阿卜杜勒·阿齐兹大学科学与技术学院统计学项目）
Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. Firstly, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real part and three imaginary parts of QDCT coefficients respectively. Then, additional Markov features are …
SALIENT: Frequency-Aware Paired Diffusion for Controllable Long-Tail CT Detection
Trauma Radiology AI Laboratory (TRAIL)（创伤放射AI实验室）；Department of Radiology and Nuclear Medicine（放射科与核医学科）；University of Maryland School of Medicine（马里兰大学医学院）
New York University（纽约大学）；PSL University（巴黎文理研究大学）；Collège de France（法兰西公学院）；Flatiron Institute（熨斗研究所）
Deep neural networks can learn powerful prior probability models for images, as evidenced by the high-quality generations obtained with recent score-based diffusion methods. But the means by which these networks capture complex global statistical structure, apparently without suffering from the curse of dimensionality, remain a mystery. To study this, we incorporate diffusion methods into a multi-scale decomposition, reducing dimensionality by assuming a stationary local Markov model for wavelet coefficients conditioned on coarser-scale coefficients. We instantiate this model using convolution…
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising
School of Biomedical Engineering, Southern Medical University（南方医科大学生物医学工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Nanfang PET Center, Nanfang Hospital Southern Medical University（南方医科大学南芳医院PET中心）
MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models
Technical University of Munich (TUM)（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Imperial College London（帝国理工学院）；Florida State University（佛罗里达州立大学）；Institute for Advanced Study, TUM (TUM-IAS)（慕尼黑工业大学高等研究院）；TUM University Hospital（慕尼黑工业大学医院）
Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（人机动力学与机器人研究所，卡尔斯鲁厄技术大学）；Department of Mechanical Engineering, Massachusetts Institute of Technology（机械工程系，麻省理工学院）；Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology（脑与认知科学系，麻省理工学院）
