IEEE Transactions on Computational Imaging最新文献_第5页

Looking Around Flatland: End-to-End 2D Real-Time NLOS Imaging 环视平面：端到端2D实时NLOS成像

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-29 DOI: 10.1109/TCI.2025.3536092

María Peña;Diego Gutierrez;Julio Marco

{"title":"Looking Around Flatland: End-to-End 2D Real-Time NLOS Imaging","authors":"María Peña;Diego Gutierrez;Julio Marco","doi":"10.1109/TCI.2025.3536092","DOIUrl":"https://doi.org/10.1109/TCI.2025.3536092","url":null,"abstract":"Time-gated non-line-of-sight (NLOS) imaging methods reconstruct scenes hidden around a corner by inverting the optical path of indirect photons measured at visible surfaces. These methods are, however, hindered by intricate, time-consuming calibration processes involving expensive capture hardware. Simulation of transient light transport in synthetic 3D scenes has become a powerful but computationally-intensive alternative for analysis and benchmarking of NLOS imaging methods. NLOS imaging methods also suffer from high computational complexity. In our work, we rely on dimensionality reduction to provide a real-time simulation framework for NLOS imaging performance analysis. We extend steady-state light transport in self-contained 2D worlds to take into account the propagation of time-resolved illumination by reformulating the transient path integral in 2D. We couple it with the recent phasor-field formulation of NLOS imaging to provide an end-to-end simulation and imaging pipeline that incorporates different NLOS imaging camera models. Our pipeline yields real-time NLOS images and progressive refinement of light transport simulations. We allow comprehensive control on a wide set of scene, rendering, and NLOS imaging parameters, providing effective real-time analysis of their impact on reconstruction quality. We illustrate the effectiveness of our pipeline by validating 2D counterparts of existing 3D NLOS imaging experiments, and provide an extensive analysis of imaging performance including a wider set of NLOS imaging conditions, such as filtering, reflectance, and geometric features in NLOS imaging setups.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"189-200"},"PeriodicalIF":4.2,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Bidirectional Feature Enhancement Network for Continuous Space-Time Video Super-Resolution 连续时空视频超分辨率的双双向特征增强网络

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-29 DOI: 10.1109/TCI.2025.3531717

Laigan Luo;Benshun Yi;Zhongyuan Wang;Zheng He;Chao Zhu

{"title":"Dual Bidirectional Feature Enhancement Network for Continuous Space-Time Video Super-Resolution","authors":"Laigan Luo;Benshun Yi;Zhongyuan Wang;Zheng He;Chao Zhu","doi":"10.1109/TCI.2025.3531717","DOIUrl":"https://doi.org/10.1109/TCI.2025.3531717","url":null,"abstract":"Space-time video super-resolution aims to reconstruct the high-frame-rate and high-resolution video from the corresponding low-frame-rate and low-resolution counterpart. Currently, the task faces the challenge of efficiently extracting long-range temporal information from available frames. Meanwhile, existing methods can only produce results for a specific moment and cannot interpolate high-resolution frames for consecutive time stamps. To address these issues, we propose a multi-stage feature enhancement method that better utilizes the limited spatio-temporal information subject to the efficiency constraint. Our approach involves a pre-alignment module that extracts coarse aligned features from the adjacent odd-numbered frames in the first stage. In the second stage, we use a bidirectional recurrent module to refine the aligned features by exploiting the long-range information from all input frames while simultaneously performing video frame interpolation. The proposed video frame interpolation module concatenates temporal information with spatial features to achieve continuous interpolation, which refines the interpolated feature progressively and enhances the spatial information by utilizing the features of different scales. Extensive experiments on various benchmarks demonstrate that the proposed method outperforms state-of-the-art in both quantitative metrics and visual effects.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"228-236"},"PeriodicalIF":4.2,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clip-Driven Universal Model for Multi-Material Decomposition in Dual-Energy CT 双能CT中多材料分解的夹驱动通用模型

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-20 DOI: 10.1109/TCI.2025.3531707

Xianghong Wang;Jiajun Xiang;Aihua Mao;Jiayi Xie;Peng Jin;Mingchao Ding;Yixuan Yuan;Yanye Lu;Lequan Yu;Hongmin Cai;Baiying Lei;Tianye Niu

{"title":"Clip-Driven Universal Model for Multi-Material Decomposition in Dual-Energy CT","authors":"Xianghong Wang;Jiajun Xiang;Aihua Mao;Jiayi Xie;Peng Jin;Mingchao Ding;Yixuan Yuan;Yanye Lu;Lequan Yu;Hongmin Cai;Baiying Lei;Tianye Niu","doi":"10.1109/TCI.2025.3531707","DOIUrl":"https://doi.org/10.1109/TCI.2025.3531707","url":null,"abstract":"Dual-energy computed tomography (DECT) offers quantitative insights and facilitates material decomposition, aiding in precise diagnosis and treatment planning. However, existing methods for material decomposition, often tailored to specific material types, need more generalizability and increase computational load with each additional material. We propose a CLIP-Driven Universal Model for adaptive Multi-Material Decomposition (MMD) to tackle this challenge. This model utilizes the semantic capabilities of text embeddings from Contrastive Language-Image Pre-training (CLIP), allowing a single network to manage structured feature embedding for multiple materials. A novel Siamese encoder and differential map fusion technique have also been integrated to enhance the decomposition accuracy while maintaining robustness across various conditions. Experiments on the simulated and physical patient studies have evidenced our model's superiority over traditional methods. Notably, it has significantly improved the Dice Similarity Coefficient—4.1%. These results underscore the potential of our network in clinical MMD applications, suggesting a promising avenue for enhancing DECT imaging analysis.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"349-361"},"PeriodicalIF":4.2,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation-Denoising Integration Network Architecture With Updated Parameter for MRI Reconstruction 基于更新参数的MRI重构估计-去噪集成网络结构

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-17 DOI: 10.1109/TCI.2025.3531729

Tingting Wu;Simiao Liu;Hao Zhang;Tieyong Zeng

{"title":"Estimation-Denoising Integration Network Architecture With Updated Parameter for MRI Reconstruction","authors":"Tingting Wu;Simiao Liu;Hao Zhang;Tieyong Zeng","doi":"10.1109/TCI.2025.3531729","DOIUrl":"https://doi.org/10.1109/TCI.2025.3531729","url":null,"abstract":"In recent years, plug-and-play (PnP) approaches have emerged as an appealing strategy for recovering magnetic resonance imaging. Compared with traditional compressed sensing methods, these approaches can leverage innovative denoisers to exploit the richer structure of medical images. However, most state-of-the-art networks are not able to adaptively remove noise at each level. To solve this problem, we propose a joint denoising network based on PnP trained to evaluate the noise distribution, realizing efficient, flexible, and accurate reconstruction. The ability of the first subnetwork to estimate complex distributions is utilized to implicitly learn noisy features, effectively tackling the difficulty of precisely delineating the obscure noise law. The second subnetwork builds on the first network and can denoise and reconstruct the image after obtaining the noise distribution. Precisely, the hyperparameter is dynamically adjusted to regulate the denoising level throughout each iteration, ensuring the convergence of our model. This step can gradually remove the image noise and use previous knowledge extracted from the frequency domain to enhance spatial particulars simultaneously. The experimental results significantly improve quantitative metrics and visual performance on different datasets.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"142-153"},"PeriodicalIF":4.2,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Denoising Knowledge Transfer Model for Zero-Shot MRI Reconstruction 零射击MRI重构的去噪知识转移模型

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-10 DOI: 10.1109/TCI.2025.3525960

Ruizhi Hou;Fang Li

{"title":"Denoising Knowledge Transfer Model for Zero-Shot MRI Reconstruction","authors":"Ruizhi Hou;Fang Li","doi":"10.1109/TCI.2025.3525960","DOIUrl":"https://doi.org/10.1109/TCI.2025.3525960","url":null,"abstract":"Though fully-supervised deep learning methods have made remarkable achievements in accelerated magnetic resonance imaging (MRI) reconstruction, the fully-sampled or high-quality data is unavailable in many scenarios. Zero-shot learning enables training on under-sampled data. However, the limited information in under-sampled data inhibits the neural network from realizing its full potential. This paper proposes a novel learning framework to enhance the diversity of the learned prior in zero-shot learning and improve the reconstruction quality. It consists of three stages: multi-weighted zero-shot ensemble learning, denoising knowledge transfer, and model-guided reconstruction. In the first stage, the ensemble models are trained using a multi-weighted loss function in k-space, yielding results with higher quality and diversity. In the second stage, we propose to use the deep denoiser to distill the knowledge in the ensemble models. Additionally, the denoiser is initialized using weights pre-trained on nature images, combining external knowledge with the information from under-sampled data. In the third stage, the denoiser is plugged into the iteration algorithm to produce the final reconstructed image. Extensive experiments demonstrate that our proposed framework surpasses existing zero-shot methods and can flexibly adapt to different datasets. In multi-coil reconstruction, our proposed zero-shot learning framework outperforms the state-of-the-art denoising-based methods.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"52-64"},"PeriodicalIF":4.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Scene Reconstruction for Color Spike Camera via Zero-Shot Learning 基于零镜头学习的彩色Spike相机动态场景重建

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-10 DOI: 10.1109/TCI.2025.3527156

Yanchen Dong;Ruiqin Xiong;Xiaopeng Fan;Shuyuan Zhu;Jin Wang;Tiejun Huang

{"title":"Dynamic Scene Reconstruction for Color Spike Camera via Zero-Shot Learning","authors":"Yanchen Dong;Ruiqin Xiong;Xiaopeng Fan;Shuyuan Zhu;Jin Wang;Tiejun Huang","doi":"10.1109/TCI.2025.3527156","DOIUrl":"https://doi.org/10.1109/TCI.2025.3527156","url":null,"abstract":"As a neuromorphic vision sensor with ultra-high temporal resolution, spike camera shows great potential in high-speed imaging. To capture color information of dynamic scenes, color spike camera (CSC) has been invented with a Bayer-pattern color filter array (CFA) on the sensor. Some spike camera reconstruction methods try to train end-to-end models by massive synthetic data pairs. However, there are gaps between synthetic and real-world captured data. The distribution of training data impacts model generalizability. In this paper, we propose a zero-shot learning-based method for CSC reconstruction to restore color images from a Bayer-pattern spike stream without pre-training. As the Bayer-pattern spike stream consists of binary signal arrays with missing pixels, we propose to leverage temporally neighboring spike signals of frame, pixel and interval levels to restore color channels. In particular, we employ a zero-shot learning-based scheme to iteratively refine the output via temporally neighboring spike stream clips. To generate high-quality pseudo-labels, we propose to exploit temporally neighboring pixels along the motion direction to estimate the missing pixels. Besides, a temporally neighboring spike interval-based representation is developed to extract temporal and color features from the binary Bayer-pattern spike stream. Experimental results on real-world captured data demonstrate that our method can restore color images with better visual quality than compared methods.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"129-141"},"PeriodicalIF":4.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Throughput Decomposition-Inspired Deep Unfolding Network for Image Compressed Sensing 基于高通量分解的图像压缩感知深度展开网络

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-09 DOI: 10.1109/TCI.2025.3527880

Tiancheng Li;Qiurong Yan;Yi Li;Jinwei Yan

{"title":"High-Throughput Decomposition-Inspired Deep Unfolding Network for Image Compressed Sensing","authors":"Tiancheng Li;Qiurong Yan;Yi Li;Jinwei Yan","doi":"10.1109/TCI.2025.3527880","DOIUrl":"https://doi.org/10.1109/TCI.2025.3527880","url":null,"abstract":"Deep Unfolding Network (DUN) has achieved great success in the image Compressed Sensing (CS) field benefiting from its great interpretability and performance. However, existing DUNs suffer from limited information transmission capacity with increasingly complex structures, leading to undesirable results. Besides, current DUNs are mostly established based on one specific optimization algorithm, which hampers the development and understanding of DUN. In this paper, we propose a new unfolding formula combining the Approximate Message Passing algorithm (AMP) and Range-Nullspace Decomposition (RND), which offers new insights for DUN design. To maximize information transmission and utilization, we propose a novel High-Throughput Decomposition-Inspired Deep Unfolding Network (HTDIDUN) based on the new formula. Specifically, we design a powerful Nullspace Information Extractor (NIE) with high-throughput transmission and stacked residual channel attention blocks. By modulating the dimension of the feature space, we provide three implementations from small to large. Extensive experiments on natural and medical images manifest that our HTDIDUN family members outperform other state-of-the-art methods by a large margin. Our codes and pre-trained models are available on GitHub to facilitate further exploration.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"89-100"},"PeriodicalIF":4.2,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast and Robust Phase Retrieval via Deep Expectation-Consistent Approximation 基于深度期望-一致近似的快速鲁棒相位检索

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-08 DOI: 10.1109/TCI.2025.3527140

Saurav K. Shastri;Philip Schniter

{"title":"Fast and Robust Phase Retrieval via Deep Expectation-Consistent Approximation","authors":"Saurav K. Shastri;Philip Schniter","doi":"10.1109/TCI.2025.3527140","DOIUrl":"https://doi.org/10.1109/TCI.2025.3527140","url":null,"abstract":"Accurately recovering images from phaseless measurements is a challenging and long-standing problem. In this work, we present “deepECpr,” which combines expectation-consistent (EC) approximation with deep denoising networks to surpass state-of-the-art phase-retrieval methods in both speed and accuracy. In addition to applying EC in a non-traditional manner, deepECpr includes a novel stochastic damping scheme that is inspired by recent diffusion methods. Like existing phase-retrieval methods based on plug-and-play priors, regularization by denoising, or diffusion, deepECpr iterates a denoising stage with a measurement-exploitation stage. But unlike existing methods, deepECpr requires far fewer denoiser calls. We compare deepECpr to the state-of-the-art prDeep (Metzler et al., 2018), Deep-ITA (Wang et al., 2020), DOLPH (Shoushtari et al., 2023), and Diffusion Posterior Sampling (Chung et al., 2023) methods for noisy phase-retrieval of color, natural, and unnatural grayscale images on oversampled-Fourier and coded-diffraction-pattern measurements and find improvements in both PSNR and SSIM with significantly fewer denoiser calls.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"116-128"},"PeriodicalIF":4.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guided Depth Inpainting in ToF Image Sensing Based on Near Infrared Information 基于近红外信息的ToF图像传感制导深度着色

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-08 DOI: 10.1109/TCI.2025.3527159

Amina Achaibou;Filiberto Pla;Javier Calpe

{"title":"Guided Depth Inpainting in ToF Image Sensing Based on Near Infrared Information","authors":"Amina Achaibou;Filiberto Pla;Javier Calpe","doi":"10.1109/TCI.2025.3527159","DOIUrl":"https://doi.org/10.1109/TCI.2025.3527159","url":null,"abstract":"Accurate depth estimation is crucial in various computer vision applications, such as robotics, augmented reality, or autonomous driving. Despite the common use of Time-of-Flight (ToF) sensing systems, they still face challenges such as invalid pixels and missing depth values, particularly with low light reflectance, distant objects, or light-saturated conditions. Cameras using indirect ToF technology provide depth maps along with active infrared brightness images, which can offer a potential guide for depth restoration in fusion approaches. This study proposes a method for depth completion by combining depth and active infrared images in ToF systems. The approach is based on a belief propagation strategy to extend valid nearby information in missing depth regions, using the infrared gradient for depth consistency. Emphasis is placed on considering object edges, especially those coinciding with depth discontinuities, to approximate missing values. Empirical results demonstrate the efficiency and simplicity of the proposed algorithm, showcasing superior outcomes compared to other reference guided depth inpainting methods.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"11 ","pages":"154-169"},"PeriodicalIF":4.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2025-01-08 DOI: 10.1109/TCI.2024.3525385

引用次数: 0