Recent event-based image reconstruction methods predominantly rely on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to process complementary event information. However, these architectures face fundamental limitations: CNNs often fail to capture global feature correlations, whereas ViTs incur quadratic computational complexity (e.g., $O(n^2)$), hindering their application in high-resolution scenarios. To address these bottlenecks, we introduce EmambaIR, an Efficient visual State Space Model designed for image reconstruction using spatially sparse and temporally continuous event streams. Our framework introduces two key components: the cross-modal Top-k Sparse Attention Module (TSAM) and the Gated State-Space Module (GSSM). TSAM efficiently performs pixel-level top-k sparse attention to guide cross-modal interactions, yielding rich yet sparse fusion features. Subsequently, GSSM utilizes a nonlinear gated unit to enhance the temporal representation of vanilla linear-complexity ($O(n)$) SSMs, effectively capturing global contextual dependencies without the typical computational overhead. Extensive experiments on six datasets across three diverse image reconstruction tasks - motion deblurring, deraining, and High Dynamic Range (HDR) enhancement - demonstrate that EmambaIR significantly outperforms state-of-the-art methods while offering substantial reductions in memory consumption and computational cost. The source code and data are publicly available at: https://github.com/YunhangWickert/EmambaIR
On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院Athinoula A. Martinos生物医学成像中心）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）
Dalian University of Technology（大连理工大学）；City University of Hong Kong（香港城市大学）
Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show …
MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction
1 Hawkes Institute, UCL \, 2 Dept. of Medical Physics；Biomedical Engineering, UCL 3 Dept. of Computer Science, UCL 4 Centre for AI, DS\&AI, AstraZeneca, UK
Ecole Centrale de Lyon（里昂中央理工学院）；Beihang University（北京航空航天大学）
Occlusions are very common in face images in the wild, leading to the degraded performance of face-related tasks. Although much effort has been devoted to removing occlusions from face images, the varying shapes and textures of occlusions still challenge the robustness of current methods. As a result, current methods either rely on manual occlusion masks or only apply to specific occlusions. This paper proposes a novel face de-occlusion model based on face segmentation and 3D face reconstruction, which automatically removes all kinds of face occlusions with even blurred boundaries,e.g., hairs.…
LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction
School of Mathematical Sciences, Capital Normal University（首都师范大学数学科学学院）；National Center for Applied Mathematics Beijing, Capital Normal University（首都师范大学北京国家应用数学中心）；Academy for Multidisciplinary Studies, Capital Normal University（首都师范大学交叉科学研究院）
Toward High-Fidelity Visual Reconstruction: From EEG-Based Conditioned Generation to Joint-Modal Guided Rebuilding
Beijing University of Technology, Beijing（北京理工大学）；Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing（北京计算智能与智能系统重点实验室）
School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology, Zhejiang University（浙江大学数字智能服务技术重点实验室）；ZWSOFT Co., Ltd.（ZWSOFT公司）
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising
School of Computer and Big Data, Minjiang University（闽江学院计算机与大数据学院）；School of Information, Renmin University of China（中国人民大学信息学院）；RMIT University（皇家墨尔本理工大学）；Victoria University（维多利亚大学）
Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet. This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR starts with a detection module that defines a few criteria to judge the abnormality of the output of deepfakes, and then uses it to guide a learnable reconstruction procedure. Adaptive masks are extracted to capture the…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；Preferred Networks
This work investigates the use of guided attention in the reconstruction of CTvolumes from biplanar DRRs. We try to improve the visual image quality of the CT reconstruction using Guided Attention based GANs (GA-GAN). We also consider the use of Vector Quantization (VQ) for the CT reconstruction so that the memory usage can be reduced, maintaining the same visual image quality. To the best of our knowledge no work has been done before that explores the Vector Quantization for this purpose. Although our findings show that our approaches outperform the previous works, still there is a lot of roo…
HAVIR: HierArchical Vision to Image Reconstruction using CLIP-Guided Versatile Diffusion
Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（医学人工智能研究中心，深圳先进技术研究所，中国科学院）；Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（生物医学成像研究中心，深圳先进技术研究所，中国科学院）
VGG-Tex: A Vivid Geometry-Guided Facial Texture Estimation Model for High Fidelity Monocular 3D Face Reconstruction
Renmin University of China（中国人民大学）；Tsinghua University（清华大学）；Beihang University（北京航空航天大学）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）
Beihang University（北京航空航天大学）；University of Edinburgh（爱丁堡大学）
The availability of high-speed 3D video sensors has greatly facilitated 3D shape acquisition of dynamic and deformable objects, but high frame rate 3D reconstruction is always degraded by spatial noise and temporal fluctuations. This paper presents a simple yet powerful intensity video guided multi-frame 4D fusion pipeline. Temporal tracking of intensity image points (of moving and deforming objects) allows registration of the corresponding 3D data points, whose 3D noise and fluctuations are then reduced by spatio-temporal multi-frame 4D fusion. We conducted simulated noise tests and real expe…
We develop a new algorithm to perform facial reconstruction from a given skull. This technique has forensic application in helping the identification of skeletal remains when other information is unavailable. Unlike most existing strategies that directly reconstruct the face from the skull, we utilize a database of portrait photos to create many face candidates, then perform a superimposition to get a well matched face, and then revise it according to the superimposition. To support this pipeline, we build an effective autoencoder for image-based facial reconstruction, and a generative model f…
DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）
We propose a deep learning system for attention-guided dual-layer image compression (AGDL). In the AGDL compression system, an image is encoded into two layers, a base layer and an attention-guided refinement layer. Unlike the existing ROI image compression methods that spend an extra bit budget equally on all pixels in ROI, AGDL employs a CNN module to predict those pixels on and near a saliency sketch within ROI that are critical to perceptual quality. Only the critical pixels are further sampled by compressive sensing (CS) to form a very compact refinement layer. Another novel CNN method is…
We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration/modeling from a single image. We observe that 3D morphable faces approach provides a reasonable geometry proxy for light position calibration. Specifically, we develop a robust optimization technique that can calibrate per-pixel lighting direction and illumination at a very high precision without assuming uniform surface albedos. Next, we apply semantic segmentation on input images and the geometry proxy to refine hairy vs. bare skin regions using tailored …
View Transformation Robustness for Multi-View 3D Object Reconstruction with Reconstruction Error-Guided View Selection
College of Computer Science and Software Engineering（计算机科学与软件工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen)（广东人工智能与数字经济实验室（深圳））
Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive Consistency Constraints
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
University of Science and Technology of China（中国科学技术大学）；Institute of Advanced Technology（先进技术研究院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Magnetic resonance imaging (MRI) with high resolution (HR) provides more detailed information for accurate diagnosis and quantitative image analysis. Despite the significant advances, most existing super-resolution (SR) reconstruction network for medical images has two flaws: 1) All of them are designed in a black-box principle, thus lacking sufficient interpretability and further limiting their practical applications. Interpretable neural network models are of significant interest since they enhance the trustworthiness required in clinical practice when dealing with medical images. 2) most ex…
University of Science and Technology of China（中国科学技术大学）
Recovery of a 3D head model including the complete face and hair regions is still a challenging problem in computer vision and graphics. In this paper, we consider this problem using only a few multi-view portrait images as input. Previous multi-view stereo methods that have been based, either on optimization strategies or deep learning techniques, suffer from low-frequency geometric structures such as unclear head structures and inaccurate reconstruction in hair regions. To tackle this problem, we propose a prior-guided implicit neural rendering network. Specifically, we model the head geomet…
The University of Queensland（昆士兰大学）；The Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Science and Technology of China（中国科学技术大学）
Magnetic resonance (MR) images exhibit various contrasts and appearances based on factors such as different acquisition protocols, views, manufacturers, scanning parameters, etc. This generally accessible appearance-related side information affects deep learning-based undersampled magnetic resonance imaging (MRI) reconstruction frameworks, but has been overlooked in the majority of current works. In this paper, we investigate the use of such side information as normalisation parameters in a convolutional neural network (CNN) to improve undersampled MRI reconstruction. Specifically, a Side Info…
Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions
ELLIS Institute Finland（埃利斯研究所芬兰分所）；Aalto University（阿alto大学）；Department of Computer Science and Engineering（计算机科学与工程系）；University at Buffalo, SUNY（布法罗大学）；University of California San Diego（加州大学圣地亚哥分校）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学 Yong Loo Lin 医学院）
Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model
Washington University in St. Louis（圣路易斯华盛顿大学）；Yale University（耶鲁大学）；Western University（西安大略大学）；Lawson Health Research Institute（劳森健康研究所）；McGill University（麦吉尔大学）；Siemens Healthineers AG（西门子医疗保健公司）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）
South China University of Technology（华南理工大学）；JD Finance America Corporation（京东金融美国公司）；Sun Yat-sen University（中山大学）
In this paper, we propose $\text{HF}^2$-VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level Memory modules in an Autoencoder with Skip Connections) to memorize normal patterns for optical flow reconstruction so that abnormal events can be sensitively identified with larger flow reconstruction errors. More importantly, conditioned on the reconstructed flows, we then employ a Conditional Variational Autoencoder (CVAE), which captures the high correlation between v…
