IEEE Transactions on Computational Imaging最新文献

{"title":"SeNAS-Net: Self-Supervised Noise and Artifact Suppression Network for Material Decomposition in Spectral CT","authors":"Xu Ji;Yuchen Lu;Yikun Zhang;Xu Zhuo;Shengqi Kan;Weilong Mao;Gouenou Coatrieux;Jean-Louis Coatrieux;Guotao Quan;Yan Xi;Shuo Li;Tianling Lyu;Yang Chen","doi":"10.1109/TCI.2024.3394772","DOIUrl":"10.1109/TCI.2024.3394772","url":null,"abstract":"For material decomposition in spectral computed tomography, the x-ray attenuation coefficient of an unknown material can be decomposed as a combination of a group of basis materials, in order to analyze its material properties. Material decomposition generally leads to amplification of image noise and artifacts. Meanwhile, it is often difficult to acquire the ground truth values of the material basis images, preventing the application of supervised learning-based noise reduction methods. To resolve such problem, we proposed a self-supervised noise and artifact suppression network for spectral computed tomography. The proposed method consists of a projection-domain self-supervised denoising network along with physics-driven constraints to mitigate the secondary artifacts, including a noise modulation item to incorporate the anisotropic noise amplitudes in the projection domain, a sinogram mask image to suppress streaky artifacts and a data fidelity loss item to further mitigate noise and to improve signal accuracy. The performance of the proposed method was evaluated based on both numerical experiment tests and laboratory experiment tests. Results demonstrated that the proposed method has promising performance in noise and artifact suppression for material decomposition in spectral computed tomography. Comprehensive ablation studies were performed to demonstrate the function of each physical constraint.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"677-689"},"PeriodicalIF":5.4,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140831029","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}

{"title":"Local Monotone Operator Learning Using Non-Monotone Operators: MnM-MOL","authors":"Maneesh John;Jyothi Rikhab Chand;Mathews Jacob","doi":"10.1109/TCI.2024.3393742","DOIUrl":"10.1109/TCI.2024.3393742","url":null,"abstract":"The recovery of magnetic resonance (MR) images from undersampled measurements is a key problem that has been the subject of extensive research in recent years. Unrolled approaches, which rely on end-to-end training of convolutional neural network (CNN) blocks within iterative reconstruction algorithms, offer state-of-the-art performance. These algorithms require a large amount of memory during training, making them difficult to employ in high-dimensional applications. Deep equilibrium (DEQ) models and the recent monotone operator learning (MOL) approach were introduced to eliminate the need for unrolling, thus reducing the memory demand during training. Both approaches require a Lipschitz constraint on the network to ensure that the forward and backpropagation iterations converge. Unfortunately, the constraint often results in reduced performance compared to the unrolled methods. The main focus of this work is to relax the constraint on the CNN block in two different ways. Inspired by convex-non-convex regularization strategies, we now impose the monotone constraint on the sum of the gradient of the data term and the CNN block, rather than constrain the CNN itself to be a monotone operator. This approach enables the CNN to learn possibly non-monotone score functions, which can translate to improved performance. In addition, we only restrict the operator to be monotone in a local neighborhood around the image manifold. Our theoretical results show that the proposed algorithm is guaranteed to converge to the fixed point and that the solution is robust to input perturbations, provided that it is initialized close to the true solution. Our empirical results show that the relaxed constraints translate to improved performance and that the approach enjoys robustness to input perturbations similar to MOL.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"742-751"},"PeriodicalIF":5.4,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830853","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}

{"title":"Towards Robustness and Efficiency of Coherence-Guided Complex Convolutional Sparse Coding for Interferometric Phase Restoration","authors":"Xiang Ding;Jian Kang;Yusong Bai;Anping Zhang;Jialin Liu;Naoto Yokoya","doi":"10.1109/TCI.2024.3393760","DOIUrl":"10.1109/TCI.2024.3393760","url":null,"abstract":"Recently, complex convolutional sparse coding (ComCSC) has demonstrated its effectiveness in interferometric phase restoration, owing to its prominent performance in noise mitigation and detailed phase preservation. By incorporating the estimated coherence into ComCSC as prior knowledge for re-weighting individual complex residues, coherence-guided complex convolutional sparse coding (CoComCSC) further improves the quality of restored phases, especially over heterogeneous land-covers with rapidly varying coherence. However, due to the exploited \u0000<inline-formula><tex-math>$L_{2}$</tex-math></inline-formula>\u0000 norm of the data fidelity term, the original CoComCSC is not robust to outliers when relatively low coherence values are sparsely distributed over high ones. We propose CoComCSC-L1 and CoComCSC-Huber to improve the robustness of CoComCSC based on the \u0000<inline-formula><tex-math>$L_{1}$</tex-math></inline-formula>\u0000 and Huber norms. Moreover, we propose an efficient solver to decrease the computational cost of solving the linear system subproblem within ComCSC-based optimization problems. By comparing the proposed methods to other state-of-the-art methods using both simulated and real data, the proposed methods demonstrate their effectiveness. Additionally, the proposed solver has the potential to improve optimization speed by approximately 10% compared to the state-of-the-art solver.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"690-699"},"PeriodicalIF":5.4,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830915","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}

{"title":"Disparity-Guided Multi-View Interaction Network for Light Field Reflection Removal","authors":"Yutong Liu;Wenming Weng;Ruisheng Gao;Zeyu Xiao;Yueyi Zhang;Zhiwei Xiong","doi":"10.1109/TCI.2024.3394773","DOIUrl":"10.1109/TCI.2024.3394773","url":null,"abstract":"Light field (LF) imaging presents a promising avenue for reflection removal, owing to its ability of reliable depth perception and utilization of complementary texture details from multiple sub-aperture images (SAIs). However, the domain shifts between real-world and synthetic scenes, as well as the challenge of embedding transmission information across SAIs pose the main obstacles in this task. In this paper, we conquer the above challenges from the perspectives of data and network, respectively. To mitigate domain shifts, we propose an efficient data synthesis strategy for simulating realistic reflection scenes, and build the largest ever LF reflection dataset containing 420 synthetic scenes and 70 real-world scenes. To enable the transmission information embedding across SAIs, we propose a novel \u0000<underline>D</u>\u0000isparity-guided \u0000<underline>M</u>\u0000ulti-view \u0000<underline>I</u>\u0000nteraction \u0000<underline>Net</u>\u0000work (DMINet) for LF reflection removal. DMINet mainly consists of a transmission disparity estimation (TDE) module and a center-side interaction (CSI) module. The TDE module aims to predict transmission disparity by filtering out reflection disturbances, while the CSI module is responsible for the transmission integration which adopts the central view as the bridge for the propagation conducted between different SAIs. Compared with existing reflection removal methods for LF input, DMINet achieves a distinct performance boost with merits of efficiency and robustness, especially for scenes with complex depth variations.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"726-741"},"PeriodicalIF":5.4,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140841932","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}

{"title":"Adaptive Local Neighborhood-Based Neural Networks for MR Image Reconstruction From Undersampled Data","authors":"Shijun Liang;Anish Lahiri;Saiprasad Ravishankar","doi":"10.1109/TCI.2024.3394770","DOIUrl":"10.1109/TCI.2024.3394770","url":null,"abstract":"Recent medical image reconstruction techniques focus on generating high-quality medical images suitable for clinical use at the lowest possible cost and with the fewest possible adverse effects on patients. Recent works have shown significant promise for reconstructing MR images from sparsely sampled k-space data using deep learning. In this work, we propose a technique that rapidly estimates deep neural networks directly at reconstruction time by fitting them on small adaptively estimated neighborhoods of a training set. In brief, our algorithm alternates between searching for neighbors in a data set that are similar to the test reconstruction, and training a local network on these neighbors followed by updating the test reconstruction. Because our reconstruction model is learned on a dataset that is in some sense similar to the image being reconstructed rather than being fit on a large, diverse training set, it is more adaptive to new scans. It can also handle changes in training sets and flexible scan settings, while being relatively fast. Our approach, dubbed LONDN-MRI, was validated on multiple data sets using deep unrolled reconstruction networks. Reconstructions were performed at four fold and eight fold undersampling of k-space with 1D variable-density random phase-encode undersampling masks. Our results demonstrate that our proposed locally-trained method produces higher-quality reconstructions compared to models trained globally on larger datasets as well as other scan-adaptive methods.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1235-1249"},"PeriodicalIF":4.2,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830855","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}

{"title":"Arbitrary-Scale Image Super-Resolution via Degradation Perception","authors":"Wenbo Wan;Zezhu Wang;Zhiyan Wang;Lingchen Gu;Jiande Sun;Qiang Wang","doi":"10.1109/TCI.2024.3393712","DOIUrl":"10.1109/TCI.2024.3393712","url":null,"abstract":"In recent years, with the rapid development of deep learning, super-resolution research oriented towards arbitrary scale (e.g., arbitrary integer and non-integer scale factors) factors has achieved great success. However, in terms of pixel space, the degradation in the same image at arbitrary scale factors is spatially variable. Similarly, the degradation is variable for different scale factors. In this paper, we propose a method that can adaptively deal with varying degradation at different scale factors, which consists of two parts. The first part, Image Refinement Network (IRN), adopts a dynamic convolution method to deal with different degradations under arbitrary scale factors on a pixel-by-pixel basis. It solves the spatial invariance problem of the ordinary convolution kernel. For well calculating the pixel mapping relationships that change during the super-resolution of arbitary scale factors, we propose a second Module, Super-Resolution Encoding Guidance Module (SREGM). It takes the high-resolution pixel space as a reference frame and uses the modelling results as prior information to better guide the high-resolution reconstruction. Extensive experiments have shown that our method achieves good results in the super-resolution of a single image with an arbitrary scale factor.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"666-676"},"PeriodicalIF":5.4,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140803012","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}

{"title":"PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction","authors":"Felix F. Zimmermann;Christoph Kolbitsch;Patrick Schuenke;Andreas Kofler","doi":"10.1109/TCI.2024.3388869","DOIUrl":"10.1109/TCI.2024.3388869","url":null,"abstract":"Quantitative Magnetic Resonance Imaging (qMRI) enables the reproducible measurement of biophysical parameters in tissue. The challenge lies in solving a nonlinear, ill-posed inverse problem to obtain the desired tissue parameter maps from acquired raw data. While various learned and non-learned approaches have been proposed, the existing learned methods fail to fully exploit the prior knowledge about the underlying MR physics, i.e. the signal model and the acquisition model. In this paper, we propose PINQI, a novel qMRI reconstruction method that integrates the knowledge about the signal, acquisition model, and learned regularization into a single end-to-end trainable neural network. Our approach is based on unrolled alternating optimization, utilizing differentiable optimization blocks to solve inner linear and non-linear optimization tasks, as well as convolutional layers for regularization of the intermediate qualitative images and parameter maps. This design enables PINQI to leverage the advantages of both the signal model and learned regularization. We evaluate the performance of our proposed network by comparing it with recently published approaches in the context of highly undersampled \u0000<inline-formula><tex-math>$T_{1}$</tex-math></inline-formula>\u0000-mapping, using both a simulated brain dataset, as well as real scanner data acquired from a physical phantom and in-vivo data from healthy volunteers. The results demonstrate the superiority of our proposed solution over existing methods and highlight the effectiveness of our method in real-world scenarios.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"628-639"},"PeriodicalIF":5.4,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10499888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593484","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}

{"title":"Fourier-Domain Inversion for the Modulo Radon Transform","authors":"Matthias Beckmann;Ayush Bhandari;Meira Iske","doi":"10.1109/TCI.2024.3388871","DOIUrl":"10.1109/TCI.2024.3388871","url":null,"abstract":"Inspired by the multiple-exposure fusion approach in computational photography, recently, several practitioners have explored the idea of high dynamic range (HDR) X-ray imaging and tomography. While establishing promising results, these approaches inherit the limitations of multiple-exposure fusion strategy. To overcome these disadvantages, the modulo Radon transform (MRT) has been proposed. The MRT is based on a co-design of hardware and algorithms. In the hardware step, Radon transform projections are folded using modulo non-linearities. Thereon, recovery is performed by algorithmically inverting the folding, thus enabling a single-shot, HDR approach to tomography. The first steps in this topic established rigorous mathematical treatment to the problem of reconstruction from folded projections. This paper takes a step forward by proposing a new, Fourier domain recovery algorithm that is backed by mathematical guarantees. The advantages include recovery at lower sampling rates while being agnostic to modulo threshold, lower computational complexity and empirical robustness to system noise. Beyond numerical simulations, we use prototype modulo ADC based hardware experiments to validate our claims. In particular, we report image recovery based on hardware measurements up to 10 times larger than the sensor's dynamic range while benefiting with lower quantization noise (\u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u000012 dB).","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"653-665"},"PeriodicalIF":5.4,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593768","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}

{"title":"Nonlocal Tensor Decomposition With Joint Low Rankness and Smoothness for Spectral CT Image Reconstruction","authors":"Chunyan Liu;Sui Li;Dianlin Hu;Jianjun Wang;Wenjin Qin;Chen Liu;Peng Zhang","doi":"10.1109/TCI.2024.3384812","DOIUrl":"10.1109/TCI.2024.3384812","url":null,"abstract":"Spectral computed tomography (CT) is a medical imaging technology that utilizes the measurement of X-ray energy absorption in human tissue to obtain image information. It can provide more accurate and detailed image information, thereby improving the accuracy of diagnosis. However, the process of spectral CT imaging is usually accompanied by a large amount of radiation and noise, which makes it difficult to obtain high-quality spectral CT image. Therefore, this paper constructs a basic third-order tensor unit based on the self-similarity of patches in the spatial domain and spectral domain while proposing nonlocal spectral CT image reconstruction methods to obtain high-quality spectral CT image. Specifically, the algorithm decomposes the recombination tensor into a low-rank tensor and a sparse tensor, which are applied by weighted tensor nuclear norm (WTNN) and weighted tensor total variation (WTTV) norm to improve the reconstruction quality, respectively. In order to further improve algorithm performance, this paper also uses weighted tensor correlated total variation regularization(WTCTV) to simultaneously characterize the low rankness and smoothness of low-rank tensor, while the sparse tensor uses weighted tensor total variation regularization (WTTV) to represent the piecewise smooth structure of the spatial domain and the similarity between pixels and adjacent frames in the spectral domain. Hence, the proposed models can effectively provide faithful underlying information of spectral CT image while maintaining spatial structure. In addition, this paper uses the Alternating Direction Method of Multipliers(ADMM) to optimize the proposed spectral CT image reconstruction models. To verify the performance of the proposed algorithms, we conducted a large number of experiments on numerical phantom and clinic patient data. The experimental results indicate that incorporating weighted regularization outperforms the results without weighted regularization, and nonlocal similarity can achieve better results than that without nonlocal similarity. Compared with existing popular algorithms, the proposed models significantly reduce running time and improve the quality of spectral CT image, thereby assisting doctors in more accurate diagnosis and treatment of diseases.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"613-627"},"PeriodicalIF":5.4,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593186","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}

{"title":"Frequency-Spatial Domain Feature Fusion for Spectral Super-Resolution","authors":"Lishan Tan;Renwei Dian;Shutao Li;Jinyang Liu","doi":"10.1109/TCI.2024.3384811","DOIUrl":"https://doi.org/10.1109/TCI.2024.3384811","url":null,"abstract":"The purpose of spectral super-resolution (SSR) is to reconstruct hyperspectral image (HSI) from RGB image, which significantly reduces the difficulty of acquiring HSI. Most existing SSR methods adopt convolutional neural networks (CNNs) as the basic framework. The capability of CNNs to capture global context is limited, which constrains the performance of SSR. In this paper, we propose a novel frequency-spatial domain feature fusion network (FSDFF) for SSR, which simultaneously learns and fuses the frequency and spatial domain features of HSI. Frequency domain features can reflect the global information of image, which can be used to obtain the global context of HSI, thereby alleviating the limitations of CNNs in capturing global context. Spatial domain features contain abundant local structural information, which is beneficial for preserving spatial details in the SSR task. The mutual fusion of the two can better model the interrelationship between HSI and RGB image, thereby achieving better SSR performance. In FSDFF, we design a frequency domain feature learning branch (FDFL) and a spatial domain feature learning branch (SDFL) to learn the frequency and spatial domain features of HSI. Furthermore, a cross-domain feature fusion module (CDFF) is designed to facilitate the complementary fusion of the two types of features. The experimental results on two public datasets indicate that FSDFF has achieved state-of-the-art performance.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"589-599"},"PeriodicalIF":5.4,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140555973","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}