IEEE Transactions on Computational Imaging最新文献_第8页

Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics 利用可变潜动力对时空流进行单像素成像

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-29 DOI: 10.1109/TCI.2024.3434541

Aleksei Sholokhov;Saleh Nabi;Joshua Rapp;Steven L. Brunton;J. Nathan Kutz;Petros T. Boufounos;Hassan Mansour

{"title":"Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics","authors":"Aleksei Sholokhov;Saleh Nabi;Joshua Rapp;Steven L. Brunton;J. Nathan Kutz;Petros T. Boufounos;Hassan Mansour","doi":"10.1109/TCI.2024.3434541","DOIUrl":"10.1109/TCI.2024.3434541","url":null,"abstract":"Imaging dynamic spatio-temporal flows typically requires high-speed, high-resolution sensors that may be physically or economically prohibitive. Single-pixel imaging (SPI) has emerged as a low-cost acquisition technique where light from a scene is projected through a spatial light modulator onto a single photodiode with a high temporal acquisition rate. The scene is then reconstructed from the temporal samples using computational techniques that leverage prior assumptions on the scene structure. In this paper, we propose to image spatio-temporal flows from incomplete measurements by leveraging scene priors in the form of a reduced-order model (ROM) of the dynamics learned from training data examples. By combining SPI acquisition with the ROM prior implemented as a neural ordinary differential equation, we achieve high-quality image sequence reconstruction with significantly reduced data requirements. Specifically, our approach achieves similar performance levels to leading methods despite using one to two orders of magnitude fewer samples. We demonstrate superior reconstruction at low sampling rates for simulated trajectories governed by Burgers' equation, Kolmogorov flow, and turbulent plumes emulating gas leaks.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1124-1138"},"PeriodicalIF":4.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869571","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

Region-Based Spectral-Spatial Mutual Induction Network for Hyperspectral Image Reconstruction 基于区域的光谱空间互感网络用于高光谱图像重建

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-18 DOI: 10.1109/TCI.2024.3430478

Jianan Li;Wangcai Zhao;Tingfa Xu

{"title":"Region-Based Spectral-Spatial Mutual Induction Network for Hyperspectral Image Reconstruction","authors":"Jianan Li;Wangcai Zhao;Tingfa Xu","doi":"10.1109/TCI.2024.3430478","DOIUrl":"10.1109/TCI.2024.3430478","url":null,"abstract":"In hyperspectral compression imaging, the choice of a reconstruction algorithm is critical for achieving high-quality results. Hyperspectral Images (HSI) have strong spectral-spatial correlations within local regions, valuable for reconstruction. However, existing learning-based methods often overlook regional variations by treating the entire image as a whole. To address this, we propose a novel region-based iterative approach for HSI reconstruction. We introduce a deep unfolding method augmented with a Region-based Spectral-Spatial Mutual Induction (RSSMI) network to model regional priors. Our approach involves partitioning the image into regions during each unfolding phase. Within each region, we employ a spatial-guided spectral attention module for holistic spectral relationships and a spectral-guided spatial attention module for spatial details. By leveraging mutual induction, our method simultaneously recovers spectral and spatial information. Furthermore, we address the issue of favoring easy-to-learn regions by introducing Focal Region Loss that dynamically adjusts loss weights for regions, emphasizing those that are harder to reconstruct. Experimental results demonstrate that our method achieves competitive performance and excels in spectrum and texture reconstruction on both simulated and real HSI datasets.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1139-1151"},"PeriodicalIF":4.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740335","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

Super-Resolution in Low Dose X-ray CT via Focal Spot Mitigation With Generative Diffusion Networks 利用生成扩散网络在低剂量 X 射线 CT 中减轻病灶的超级分辨率

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-18 DOI: 10.1109/TCI.2024.3430487

Carlos M. Restrepo-Galeano;Gonzalo R. Arce

{"title":"Super-Resolution in Low Dose X-ray CT via Focal Spot Mitigation With Generative Diffusion Networks","authors":"Carlos M. Restrepo-Galeano;Gonzalo R. Arce","doi":"10.1109/TCI.2024.3430487","DOIUrl":"10.1109/TCI.2024.3430487","url":null,"abstract":"Advancing the resolution capabilities of X-ray CT imaging, particularly in low-dose applications, is a paramount pursuit in the field. This quest for superior spatial detail is hindered by the pervasive issue of focal spot blooming, which plagues medical scanners due to the finite nature of the emittance surface in X-ray sources. Such a phenomenon introduces optical distortions in the measurements that limit the achievable resolution. In response to this challenge, we introduce a novel approach: Focal Spot Diffusion CT (FSD-CT). Unlike traditional methods that rely on limited and simplified idealizations of X-ray models, FSD-CT adopts a more complex, realistic representation of X-ray sources. FSD-CT leverages a generative diffusion-based reconstruction framework, guided by a forward imaging model for sample consistency and a frequency selection module for enhanced spectral content. FSD-CT successfully mitigates focal spot blooming without imposing a significant computational burden when compared to other diffusion-based reconstruction methods, offering a versatile solution for improving CT resolution. Computational experiments using simulations based on commercial medical scanners show FSD-CT delivers gains of up to 4 dB in fan-beam tomography compared to benchmarks such as filtered backprojection, end-to-end CNNs, and state-of-the-art diffusion models. The technique's robustness is confirmed in challenging scenarios, including sparse angle CT, off-distribution samples, and reconstructions from real projections. FSD-CT helps to overcome limitations in spatial resolution and offers a plausible solution that could be applied in clinical CT imaging after more in-depth studies are conducted.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1111-1123"},"PeriodicalIF":4.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740334","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

Side Information-Assisted Low-Dose CT Reconstruction 侧面信息辅助低剂量 CT 重建

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-18 DOI: 10.1109/TCI.2024.3430469

Yuanke Zhang;Rujuan Cao;Fan Xu;Rui Zhang;Fengjuan Jiang;Jing Meng;Fei Ma;Yanfei Guo;Jianlei Liu

{"title":"Side Information-Assisted Low-Dose CT Reconstruction","authors":"Yuanke Zhang;Rujuan Cao;Fan Xu;Rui Zhang;Fengjuan Jiang;Jing Meng;Fei Ma;Yanfei Guo;Jianlei Liu","doi":"10.1109/TCI.2024.3430469","DOIUrl":"10.1109/TCI.2024.3430469","url":null,"abstract":"CT images from individual patients or different patient populations typically share similar radiological features such as textures and structures. In model-based iterative reconstruction (MBIR) for low-dose CT (LDCT) imaging, image quality enhancement can be achieved not only by relying on the intrinsic raw data, but also by incorporating side information extracted from high-quality normal-dose CT (NDCT) exemplar images. The additional side information helps overcome the inherent limitations of raw data in low-dose scanning and offers potential improvements in LDCT image quality. This study investigates the effectiveness of side information-assisted MBIR (SI-MBIR) in enhancing the quality of LDCT images. Specifically, we propose to use the noise-free exemplar images to generate side information that aligns with the structural features of regions of interest (ROIs) in the target image. Each ROI is enhanced with a custom-designed prior subspace that is derived from similar exemplar samples and reflects its unique structural and textural characteristics. We then propose an adaptive sparse modeling approach, in particular, a weighted Laplace distribution model for the prior subspace. The weighted Laplace model is carefully tuned to match the signal-to-noise ratio (SNR) of each transform band, allowing adaptive sparse modeling on different bands. Furthermore, we propose an efficient CT reconstruction algorithm based on this adaptive sparse model. Using the alternating direction method of multipliers (ADMM) framework, an optimization method for this reconstruction algorithm has been formulated. Extensive experimental studies were conducted to validate the effectiveness of the proposed algorithm. The results show that the proposed algorithm can achieve noticeable improvements over some state-of-the-art MBIR methods in terms of noise suppression and texture preservation.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1080-1093"},"PeriodicalIF":4.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740336","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

DTDeMo: A Deep Learning-Based Two-Stage Image Demosaicing Model With Interpolation and Enhancement DTDeMo：基于深度学习的两阶段图像去马赛克插值与增强模型

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-10 DOI: 10.1109/TCI.2024.3426360

Jingchao Hou;Garas Gendy;Guo Chen;Liangchao Wang;Guanghui He

{"title":"DTDeMo: A Deep Learning-Based Two-Stage Image Demosaicing Model With Interpolation and Enhancement","authors":"Jingchao Hou;Garas Gendy;Guo Chen;Liangchao Wang;Guanghui He","doi":"10.1109/TCI.2024.3426360","DOIUrl":"10.1109/TCI.2024.3426360","url":null,"abstract":"Image demosaicing is one of the most ubiquitous and performance-critical image processing tasks. However, traditional demosaicing methods use fixed weights to finish the interpolation, while deep learning demosaicing restoration always breaks the image array arrangement rule, and they can't fully use the existing pixel information. To rectify these weaknesses, in this paper, we propose the convolution interpolation block (CIB) to obey the RAW data arrangement rule and the deep demosaicing residual block (DDRB) to repeatedly utilize existing pixel information for demosaicing. Based on the CIB and DDRB, we present a novel two-stage demosaicing model (DTDeMo), including differential interpolation and enhancement processes. Specifically, the interpolation process contains several CIBs and DDRBs with trainable interpolation parameters. Meanwhile, the enhancement process consists of a transformer-based block and a series of DDRBs to enhance the interpolation results. The effectiveness of CIBs, DDRBs, the proposed interpolation process, and the enhancement process is confirmed through an ablation study. A thorough comparison with several methods shows that our DTDeMo outperforms state of the art quantitatively and qualitatively.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1026-1039"},"PeriodicalIF":4.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141588024","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

No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy 无清洁参照图像超分辨率：电子显微镜应用

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-10 DOI: 10.1109/TCI.2024.3426349

Mohammad Khateri;Morteza Ghahremani;Alejandra Sierra;Jussi Tohka

{"title":"No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy","authors":"Mohammad Khateri;Morteza Ghahremani;Alejandra Sierra;Jussi Tohka","doi":"10.1109/TCI.2024.3426349","DOIUrl":"10.1109/TCI.2024.3426349","url":null,"abstract":"The inability to acquire clean high-resolution (HR) electron microscopy (EM) images over a large brain tissue volume hampers many neuroscience studies. To address this challenge, we propose a deep-learning-based image super-resolution (SR) approach to computationally reconstruct a clean HR 3D-EM image with a large field of view (FoV) from noisy low-resolution (LR) acquisition. Our contributions are I) investigation of training with no-clean references; II) introduction of a novel network architecture, named EMSR, for enhancing the resolution of LR EM images while reducing inherent noise. The EMSR leverages distinctive features in brain EM images–repetitive textural and geometrical patterns amidst less informative backgrounds– via multiscale edge-attention and self-attention mechanisms to emphasize edge features over the background; and, III) comparison of different training strategies including using acquired LR and HR image pairs, i.e., real pairs with no-clean references contaminated with real corruptions, pairs of synthetic LR and acquired HR, as well as acquired LR and denoised HR pairs. Experiments with nine brain datasets showed that training with real pairs can produce high-quality super-resolved results, demonstrating the feasibility of training with nonclean references. Additionally, comparable results were observed, both visually and numerically, when employing denoised and noisy references for training. Moreover, utilizing the network trained with synthetically generated LR images from HR counterparts proved effective in yielding satisfactory SR results, even in certain cases, outperforming training with real pairs. The proposed SR network was compared quantitatively and qualitatively with several established SR techniques, demonstrating either the superiority or competitiveness of the proposed method in recovering fine details while mitigating noise.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1094-1110"},"PeriodicalIF":4.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10592622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141588023","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

Deep Learned Non-Linear Propagation Model Regularizer for Compressive Spectral Imaging 用于压缩光谱成像的深度学习非线性传播模型规整器

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-05 DOI: 10.1109/TCI.2024.3422900

Romario Gualdrón-Hurtado;Henry Arguello;Jorge Bacca

{"title":"Deep Learned Non-Linear Propagation Model Regularizer for Compressive Spectral Imaging","authors":"Romario Gualdrón-Hurtado;Henry Arguello;Jorge Bacca","doi":"10.1109/TCI.2024.3422900","DOIUrl":"10.1109/TCI.2024.3422900","url":null,"abstract":"Coded aperture snapshot spectral imager (CASSI), efficiently captures 3D spectral images by sensing 2D projections of the scene. While CASSI offers a substantial reduction in acquisition time, compared to traditional scanning optical systems, it requires a reconstruction post-processing step. Furthermore, to obtain high-quality reconstructions, an accurate propagation model is required. Notably, CASSI exhibits a variant spatio-spectral sensor response, making it difficult to acquire an accurate propagation model. To address these inherent limitations, this work proposes to learn a deep non-linear fully differentiable propagation model that can be used as a regularizer within an optimization-based reconstruction algorithm. The proposed approach trains the non-linear spatially-variant propagation model using paired compressed measurements and spectral images, by employing side information only in the calibration step. From the deep propagation model incorporation into a plug-and-play alternating direction method of multipliers framework, our proposed method outperforms traditional CASSI linear-based models. Extensive simulations and a testbed implementation validate the efficacy of the proposed methodology.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1016-1025"},"PeriodicalIF":4.2,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573765","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

PGIUN: Physics-Guided Implicit Unrolling Network for Accelerated MRI PGIUN：用于加速核磁共振成像的物理引导隐式展开网络

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-03 DOI: 10.1109/TCI.2024.3422840

Jiawei Jiang;Zihan He;Yueqian Quan;Jie Wu;Jianwei Zheng

{"title":"PGIUN: Physics-Guided Implicit Unrolling Network for Accelerated MRI","authors":"Jiawei Jiang;Zihan He;Yueqian Quan;Jie Wu;Jianwei Zheng","doi":"10.1109/TCI.2024.3422840","DOIUrl":"10.1109/TCI.2024.3422840","url":null,"abstract":"To cope with the challenges stemming from prolonged acquisition periods, compressed sensing MRI has emerged as a popular technique to accelerate the reconstruction of high-quality images from under-sampled k-space data. Most current solutions endeavor to solve this issue with the pursuit of certain prior properties, yet the treatments are all enforced in the original space, resulting in limited feature information. To boost the performance yet with the guarantee of high running efficiency, in this study, we propose a Physics-Guided Implicit Unrolling Network (PGIUN). Specifically, by an elaborately designed reversible network, the inputs are first mapped to a channel-lifted implicit space, which taps the potential of capturing spatial-invariant features sufficiently. Within this implicit space, we then unfold an accelerated optimization algorithm to iterate an efficient and feasible solution, in which a parallelly dual-domain update is equipped for better feature fusion. Finally, an inverse embedding transformation of the recovered high-dimensional representation is employed to achieve the desired estimation. PGIUN enjoys high interpretability benefiting from the physically induced modules, which not only facilitates an intuitive understanding of the internal working mechanism but also endows it with high generalization ability. Extensive experiments conducted across diverse datasets and varying sampling rates/patterns consistently establish the superiority of our approach over state-of-the-art methods in both visual and quantitative evaluations.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1055-1068"},"PeriodicalIF":4.2,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141549933","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

Learning Task-Specific Strategies for Accelerated MRI 学习针对特定任务的加速核磁共振成像策略

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-01 DOI: 10.1109/TCI.2024.3410521

Zihui Wu;Tianwei Yin;Yu Sun;Robert Frost;Andre van der Kouwe;Adrian V. Dalca;Katherine L. Bouman

{"title":"Learning Task-Specific Strategies for Accelerated MRI","authors":"Zihui Wu;Tianwei Yin;Yu Sun;Robert Frost;Andre van der Kouwe;Adrian V. Dalca;Katherine L. Bouman","doi":"10.1109/TCI.2024.3410521","DOIUrl":"10.1109/TCI.2024.3410521","url":null,"abstract":"Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual information from subsampled measurements for diagnostic tasks. Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in a suboptimal end-to-end performance. In this work, we propose \u0000<sc>Tackle</small>\u0000 as a unified co-design framework for jointly optimizing subsampling, reconstruction, and prediction strategies for the performance on downstream tasks. The naïve approach of simply appending a task prediction module and training with a task-specific loss leads to suboptimal downstream performance. Instead, we develop a training procedure where a backbone architecture is first trained for a generic pre-training task (image reconstruction in our case), and then fine-tuned for different downstream tasks with a prediction head. Experimental results on multiple public MRI datasets show that \u0000<sc>Tackle</small>\u0000 achieves an improved performance on various tasks over traditional CS-MRI methods. We also demonstrate that \u0000<sc>Tackle</small>\u0000 is robust to distribution shifts by showing that it generalizes to a new dataset we experimentally collected using different acquisition setups from the training data. Without additional fine-tuning, \u0000<sc>Tackle</small>\u0000 leads to both numerical and visual improvements compared to existing baselines. We have further implemented a learned 4×-accelerated sequence on a Siemens 3T MRI Skyra scanner. Compared to the fully-sampling scan that takes 335 seconds, our optimized sequence only takes 84 seconds, achieving a four-fold time reduction as desired, while maintaining high performance.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1040-1054"},"PeriodicalIF":4.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531297","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

Restoration on High Turbidity Water Images Under Near-Field Illumination Using a Light-Field Camera 使用光场相机修复近场照明下的高浊度水图像

IF 4.2 2区计算机科学

IEEE Transactions on Computational Imaging Pub Date : 2024-07-01 DOI: 10.1109/TCI.2024.3420881

Shijun Zhou;Zhen Zhang;Yajing Liu;Jiandong Tian

{"title":"Restoration on High Turbidity Water Images Under Near-Field Illumination Using a Light-Field Camera","authors":"Shijun Zhou;Zhen Zhang;Yajing Liu;Jiandong Tian","doi":"10.1109/TCI.2024.3420881","DOIUrl":"10.1109/TCI.2024.3420881","url":null,"abstract":"Restoring underwater degraded images necessitates accurate estimation of backscatter. Prior research commonly treats backscatter as a constant value across channels. However, addressing backscatter removal becomes intricate when images are captured under conditions of near-field illumination and within densely scattered mediums. In these scenarios, the approximation of backscatter by constant values falls short of efficacy. This paper presents an innovative methodology for characterizing backscatter distribution using curved surfaces while taking into account the scattering conditions at the pixel level. Unlike the previous methods that employ the atmosphere scattering model, we introduce an adaptative function to describe backscatter distribution. By capitalizing on the capabilities of light field cameras in recording light directions, we devise a solution to the focus problem encountered in turbid water environments. Through shear and refocus operations, we not only achieve denoising but also elevate overall image quality. The experimental results clearly demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"984-999"},"PeriodicalIF":4.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503924","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