High-resolution Image Reconstruction Research Articles

Blind Super-Resolution of 3D MRI via Unsupervised Domain Transformation.

High-resolution medical images can be effectively used for clinical diagnosis. However, the acquisition of high-resolution images is difficult and often limited by medical instruments. Super-resolution (SR) methods provide a solution, where high-resolution (HR) images can be reconstructed from low-resolution (LR) ones. Most of existing deep neural networks for 3D SR medical images trained in a non-blind process, where LR images are directly degraded from HR data via a pre-determined downscale method. Such approaches rely heavily on the assumed degradation model, resulting in inevitable deviations in real clinical practice. Blind super-resolution, as a more attractive research line for this field, aims to generate HR images from LR inputs containing unknown degradation. Towards generalizing SR models for diverse types of degradation, we propose a robust blind SR of 3D medical images in an unsupervised manner with domain correction and upscaling treatment. First, a CycleGAN-based architecture is implemented to generate the LR data from the source domain to the target one for domain correction. Then, an upscaling network is learned via pre-determined HR-LR couples for reconstruction. The proposed framework is able to automatically learn noisy and blurry correction kernels for unpaired 3D SR magnetic resonance images (MRI). Our method achieves better and more robust performances in reconstruction of HR images from LR MRI with multiple unknown degradation processes, and show its superiority to other state-of-the-art supervised models and cycle-consistency based methods, especially in severe distortion cases.

PILN: A posterior information learning network for blind reconstruction of lung CT images

Background and objective:Computer tomography (CT) imaging technology has played significant roles in the diagnosis and treatment of various lung diseases, but the degradations in CT images usually cause the loss of detailed structural information and interrupt the judgement from clinicians. Therefore, reconstructing noise-free, high-resolution CT images with sharp details from degraded ones is of great importance for the computer-assisted diagnosis (CAD) system. However, current image reconstruction methods suffer from unknown parameters of multiple degradations in actual clinical images.Methods:To solve these problems, we propose a unified framework, so called Posterior Information Learning Network (PILN), for blind reconstruction of lung CT images. The framework consists of two stages: Firstly, a noise level learning (NLL) network is proposed to quantify the Gaussian and artifact noise degradations into different levels. Inception-residual modules are designed to extract multi-scale deep features from the noisy image, and residual self-attention structures are proposed to refine deep features to essential representations of noise. Secondly, by taking the estimated noise levels as prior information, a cyclic collaborative super-resolution (CyCoSR) network is proposed to iteratively reconstruct the high-resolution CT image and estimate the blur kernel. Two convolutional modules are designed based on cross-attention transformer structure, named as Reconstructor and Parser. The high-resolution image is restored from the degraded image by the Reconstructor under the guidance of the predicted blur kernel, while the blur kernel is estimated by the Parser according to the reconstructed image and the degraded one. The NLL and CyCoSR networks are formulated as an end-to-end framework to handle multiple degradations simultaneously.Results:The proposed PILN is applied to the Cancer Imaging Archive (TCIA) dataset and the Lung Nodule Analysis 2016 Challenge (LUNA16) dataset to evaluate its ability in reconstructing lung CT images. Compared with the state-of-the-art image reconstruction algorithms, it can provide high-resolution images with less noise and sharper details with respect to quantitative benchmarks.Conclusions:Extensive experimental results demonstrate that our proposed PILN can achieve better performance on blind reconstruction of lung CT images, providing noise-free, detail-sharp and high-resolution images without knowing the parameters of multiple degradation sources.

Interfacial debonding detection for CFST structures using an ultrasonic phased array: Application to the Shenzhen SEG building

Concrete filled steel tube (CFST) structures have been widely applied to the construction of high-rise buildings and bridges. However, interfacial debonding defects generally occur due to deficient construction quality, concrete shrinkage, and other reasons. In this paper, an imaging-based approach using a designed ultrasonic phased array is proposed for the semi-quantitative characterization of interfacial debonding defects in CFST columns. A total focusing method (TFM) is employed for high-resolution image reconstruction from the full matrix capture (FMC) dataset acquired by the phased array. Based on the reflection coefficient of the steel–concrete interface in the imaging result, a criterion is proposed to evaluate the degree of debonding damage. The proposed approach is used to evaluate the debonding defects in the 16 CFST columns of the Shenzhen SEG Building, which experienced abnormal shaking on May 18–20, 2021. The accuracy of the proposed method is fully validated with 15 boreholes on the CFST columns. Then, ultrasonic phase array measurements were conducted along 34 circumferential rings on nine of the CFST columns. The results show that the comprehensive debonding rate is as high as 46.6%. It is concluded that the proposed ultrasonic phased array method can accurately detect and locate the debonding defects in CFST structures, and this method has a broad application prospect for non-destructive inspection in civil engineering.

Text Prior Guided Scene Text Image Super-resolution.

Scene text image super-resolution (STISR) aims to improve the resolution and visual quality of low-resolution (LR) scene text images, while simultaneously boost the performance of text recognition. However, most of the existing STISR methods regard text images as natural scene images, ignoring the categorical information of text. In this paper, we make an inspiring attempt to embed text recognition prior into STISR model. Specifically, we adopt the predicted character recognition probability sequence as the text prior, which can be obtained conveniently from a text recognition model. The text prior provides categorical guidance to recover high-resolution (HR) text images. On the other hand, the reconstructed HR image can refine the text prior in return. Finally, we present a multi-stage text prior guided super-resolution (TPGSR) framework for STISR. Our experiments on the benchmark TextZoom dataset show that TPGSR can not only effectively improve the visual quality of scene text images, but also significantly improve the text recognition accuracy over existing STISR methods. Our model trained on TextZoom also demonstrates certain generalization capability to the LR images in other datasets.

Enhancing Forward-Looking Image Resolution: Combining Low-Rank and Sparsity Priors

Compressed sensing (CS)-based imaging technology has attracted a lot of interest because it can enhance imaging resolution. Targets of interest for forward-looking imaging radar are typically few in comparison to the entire imaging region. This sparsity allows for the natural application of CS to the reconstruction of high-resolution forward-looking images. However, conventional CS-based imaging methods can only perform well when the signal-to-noise ratio (SNR) is high. Strong noise in radar imaging prevents the CS-based methods from producing excellent imaging results. Inspired by the low-rank property of the received radar target echo and the sparsity of the forward-looking image targets, we present a combined low-rank and sparse prior restricted model for forward-looking imaging with a multichannel array radar to overcome strong noise. To solve the low-rank joint sparse double prior constraint optimization problem, an augmented Lagrange multiplier (ALM) reconstruction method under the framework of the alternating direction multiplier method (ADMM) is proposed. Finally, the results of simulation and real measurement data indicate that our presented method is fairly effective at enhancing the azimuth resolution and robustness of forward-looking radar imaging in comparison to other current methods.

SCTANet: A Spatial Attention-Guided CNN-Transformer Aggregation Network for Deep Face Image Super-Resolution

Numerous CNN-based algorithms have been proposed to reconstruct high-quality face images. However, the inability of convolution operation to model long-distance relationships limits the performance of the CNN-based methods. Moreover, in the high-resolution (HR) image reconstruction stage, with the well decoded feature representations, more efficient architecture design can be explored to synthesize pixel-level image details. In this work, we propose a spatial attention-guided CNN-Transformer aggregation network (SCTANet) for face image super-resolution (FSR) tasks. The core component in the deep feature extraction stage is the Hybrid Attention Aggregation (HAA) block. The HAA block has two parallel paths, one for the Residual Spatial Attention (RSA) block, the other for the Multi-scale Patch embedding and Spatial-attention Masked Transformer (MPSMT) block. The HAA block combines the strengths of CNN and transformer to effectively exploit both local and global information. For the reconstruction stage, we propose to use the Sub-pixel MLP-based Upsampling (SMU) module instead of the conventional CNN architecture. The SMU module promotes the reconstruction of pixel-level image details and reduces computational complexity. Extensive experiments on both synthetic and real-world face datasets demonstrate the superiority of our proposed SCTANet over state-of-the-art methods.

Unsupervised 3D Reconstruction with Multi-Measure and High-Resolution Loss

Multi-view 3D reconstruction technology based on deep learning is developing rapidly. Unsupervised learning has become a research hotspot because it does not need ground truth labels. The current unsupervised method mainly uses 3DCNN to regularize the cost volume to regression image depth. This approach results in high memory requirements and long computing time. In this paper, we propose an end-to-end unsupervised multi-view 3D reconstruction network framework based on PatchMatch, Unsup_patchmatchnet. It dramatically reduces memory requirements and computing time. We propose a feature point consistency loss function. We incorporate various self-supervised signals such as photometric consistency loss and semantic consistency loss into the loss function. At the same time, we propose a high-resolution loss method. This improves the reconstruction of high-resolution images. The experiment proves that the memory usage of the network is reduced by 80% and the running time is reduced by more than 50% compared with the network using 3DCNN method. The overall error of reconstructed 3D point cloud is only 0.501 mm. It is superior to most current unsupervised multi-view 3D reconstruction networks. Then, we test on different data sets and verify that the network has good generalization.

Near-Field 3D Sparse SAR Direct Imaging with Irregular Samples

Sparse imaging is widely used in synthetic aperture radar (SAR) imaging. Compared with the traditional matched filtering (MF) methods, sparse SAR imaging can directly image the scattered points of a target and effectively reduce the sidelobes and clutter in irregular samples. However, in view of the large-scale computational complexity of sparse reconstruction with raw echo data, traditional sparse reconstruction algorithms often require huge computational expense. To solve the above problems, in this paper, we propose a 3D near-field sparse SAR direct imaging algorithm for irregular trajectories, adopting a piece of preliminary information in the SAR image to update the dictionary matrix dimension, using the Gaussian iterative method, and optimizing the signal-processing techniques, which can achieve 3D sparse reconstruction in a more direct and rapid manner. The proposed algorithm was validated through simulations and empirical study of irregular scanning scenarios and compared with traditional MF and sparse reconstruction methods, and was shown to significantly reduce the computation time and effectively preserve the complex information of the scenes to achieve high-resolution image reconstruction.

A novel fuzzy hierarchical fusion attention convolution neural network for medical image super-resolution reconstruction

The clarity of medical images is crucial for doctors to identify and diagnose different diseases. High-resolution images have more detailed information and clearer content than low-resolution images. It is well known that medical images can frequently have some blurred object boundaries, and that traditional deep learning models cannot adequately describe the uncertainty of these blurred boundaries. This paper proposes a new fuzzy metric to characterize the uncertainty of pixels and designs a fuzzy hierarchical fusion attention neural network based on multiscale guided learning. Specifically, a fuzzy neural information-processing block is proposed, which converts an input image into a fuzzy domain using fuzzy membership functions. The uncertainty of the pixels is processed using the proposed fuzzy rules, and then the output of the fuzzy rule layer is fused with the result of the convolution in the neural network. Simultaneously, a multiscale guided-learning dense residual block and pyramidal hierarchical attention module are designed to extract more effective hierarchical image information. Finally, a recurrent memory module with a residual structure is used to process the output features of the hierarchical attention modules. A recursive sub-pixel reconstruction module is used at the tail of the network to reconstruct the images. Compared with existing super-resolution methods using the public COVID-CT dataset, the proposed method demonstrated superior performance in high-resolution medical image reconstruction and reduced the number of parameters and analysis time of the models.

Pump-guided nonlinear film for phase conjugation.

It is known that phase conjugation technique is achieved by the nonlinearity of materials, and widely adopted in various applications, such as high-resolution imaging, signal amplification, and target detecting. Here, we have proposed a field-enhancement method for the degenerate four wave mixing (FWM) for phase conjugation purpose. In this method, a thin film waveguide with nonlinear property is utilized to confine and guide the pumps, achieving the enhanced FWM within a flexible structure. Compared to existing degenerate FWM methods, three merits are introduced by the proposed pump-guided nonlinear film. First, the pump is confined and guided in the nonlinear waveguide, and the pump energy is concentrated to achieve high power level of the phase-conjugated signals. Second, less pump energy leaks out from the thin film, with less interference to the phase-conjugated signals. The last one is that pump-guided film can be engineered into flexible shapes for different practical applications. Based on these advantages, the phase conjugation property is numerically verified for high-resolution image reconstruction, even with damping of waveguide or in the presence of the metallic particles and the dielectric blocks.

High-resolution medical image reconstruction based on residual neural network for diagnosis of cerebral aneurysm.

Cerebral aneurysms are classified as severe cerebrovascular diseases due to hidden and critical onset, which seriously threaten life and health. An effective strategy to control intracranial aneurysms is the regular diagnosis and timely treatment by CT angiography (CTA) imaging technology. However, unpredictable patient movements make it challenging to capture sub-millimeter-level ultra-high resolution images in a CTA scan. In order to improve the doctor's judgment, it is necessary to improve the clarity of the cerebral aneurysm medical image algorithm. This paper mainly focuses on researching a three-dimensional medical image super-resolution algorithm applied to cerebral aneurysms. Although some scholars have proposed super-resolution reconstruction methods, there are problems such as poor effect and too much reconstruction time. Therefore, this paper designs a lightweight super-resolution network based on a residual neural network. The residual block structure removes the B.N. layer, which can effectively solve the gradient problem. Considering the high-resolution reconstruction needs to take the complete image as the research object and the fidelity of information, this paper selects the channel domain attention mechanism to improve the performance of the residual neural network. The new data set of cerebral aneurysms in this paper was obtained by CTA imaging technology of patients in the Department of neurosurgery, the second affiliated of Guizhou Medical University Hospital. The proposed model was evaluated from objective evaluation, model effect, model performance, and detection comparison. On the brain aneurysm data set, we tested the PSNR and SSIM values of 2 and 4 magnification factors, and the scores of our method were 33.01, 28.39, 33.06, and 28.41, respectively, which were better than those of the traditional SRCNN, ESPCN and FSRCNN. Subsequently, the model is applied to practice in this paper, and the effect, performance index and diagnosis of auxiliary doctors are obtained. The experimental results show that the high-resolution image reconstruction model based on the residual neural network designed in this paper plays a more influential role than other image classification methods. This method has higher robustness, accuracy and intuition. With the wide application of CTA images in the clinical diagnosis of cerebral aneurysms and the increasing number of application samples, this method is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of cerebral aneurysms.

Fourier ptychographic microscopy with untrained deep neural network priors.

We propose a physics-assisted deep neural network scheme in Fourier ptychographic microscopy (FPM) using untrained deep neural network priors (FPMUP) to achieve a high-resolution image reconstruction from multiple low-resolution images. Unlike the traditional training type of deep neural network that requires a large labelled dataset, this proposed scheme does not require training and instead outputs the high-resolution image by optimizing the parameters of neural networks to fit the experimentally measured low-resolution images. Besides the amplitude and phase of the sample function, another two parallel neural networks that generate the general pupil function and illumination intensity factors are incorporated into the carefully designed neural networks, which effectively improves the image quality and robustness when both the aberration and illumination intensity fluctuation are present in FPM. Reconstructions using simulated and experimental datasets are demonstrated, showing that the FPMUP scheme has better image quality than the traditional iterative algorithms, especially for the phase recovery, but at the expense of increasing computational cost. Most importantly, it is found that the FPMUP scheme can predict the Fourier spectrum of the sample outside synthetic aperture of FPM and thus eliminate the ringing effect of the recovered images due to the spectral truncation. Inspired by deep image prior in the field of image processing, we may impute the expansion of Fourier spectrums to the deep prior rooted in the architecture of the careful designed four parallel deep neural networks. We envisage that the resolution of FPM will be further enhanced if the Fourier spectrum of the sample outside the synthetic aperture of FPM is accurately predicted.

Fast parallel implementation for total variation constrained algebraic reconstruction technique.

In computed tomography (CT), the total variation (TV) constrained algebraic reconstruction technique (ART) can obtain better reconstruction quality when the projection data are sparse and noisy. However, the ART-TV algorithm remains time-consuming since it requires large numbers of iterations, especially for the reconstruction of high-resolution images. In this work, we propose a fast algorithm to calculate the system matrix for line intersection model and apply this algorithm to perform the forward-projection and back-projection operations of the ART. Then, we utilize the parallel computing techniques of multithreading and graphics processing units (GPU) to accelerate the ART iteration and the TV minimization, respectively. Numerical experiments show that our proposed parallel implementation approach is very efficient and accurate. For the reconstruction of a 2048 × 2048 image from 180 projection views of 2048 detector bins, it takes about 2.2 seconds to perform one iteration of the ART-TV algorithm using our proposed approach on a ten-core platform. Experimental results demonstrate that our new approach achieves a speedup of 23 times over the conventional single-threaded CPU implementation that using the Siddon algorithm.

High-resolution Solar Image Reconstruction Based on Non-rigid Alignment

Suppressing the interference of atmospheric turbulence and obtaining observation data with a high spatial resolution are an issue to be solved urgently for ground observations. One way to solve this problem is to perform a statistical reconstruction of short-exposure speckle images. Combining the rapidity of Shift-Add and the accuracy of speckle masking, this paper proposes a novel reconstruction algorithm-NASIR (Non-rigid Alignment based Solar Image Reconstruction). NASIR reconstructs the phase of the object image at each frequency by building a computational model between geometric distortion and intensity distribution and reconstructs the modulus of the object image on the aligned speckle images by speckle interferometry. We analyzed the performance of NASIR by using the correlation coefficient, power spectrum, and coefficient of variation of intensity profile in processing data obtained by the NVST (1 m New Vacuum Solar Telescope). The reconstruction experiments and analysis results show that the quality of images reconstructed by NASIR is close to speckle masking when the seeing is good, while NASIR has excellent robustness when the seeing condition becomes worse. Furthermore, NASIR reconstructs the entire field of view in parallel in one go, without phase recursion and block-by-block reconstruction, so its computation time is less than half that of speckle masking. Therefore, we consider NASIR is a robust and high-quality fast reconstruction method that can serve as an effective tool for data filtering and quick look.

Automated 3D reconstruction of the fetal thorax in the standard atlas space from motion-corrupted MRI stacks for 21-36 weeks GA range.

Slice-to-volume registration (SVR) methods allow reconstruction of high-resolution 3D images from multiple motion-corrupted stacks. SVR-based pipelines have been increasingly used for motion correction for T2-weighted structural fetal MRI since they allow more informed and detailed diagnosis of brain and body anomalies including congenital heart defects (Lloyd etal., 2019). Recently, fully automated rigid SVR reconstruction of the fetal brain in the atlas space was achieved in Salehi etal. (2019) that used convolutional neural networks (CNNs) for segmentation and pose estimation. However, these CNN-based methods have not yet been applied to the fetal trunk region. Meanwhile, the existing rigid and deformable SVR (DSVR) solutions (Uus etal., 2020) for the fetal trunk region are limited by the requirement of manual input as well the narrow capture range of the classical gradient descent based registration methods that cannot resolve severe fetal motion frequently occurring at the early gestational age (GA). Furthermore, in our experience, the conventional 2D slice-wise CNN-based brain masking solutions are reportedly prone to errors that require manual corrections when applied on a wide range of acquisition protocols or abnormal cases in clinical setting. In this work, we propose a fully automated pipeline for reconstruction of the fetal thorax region for 21-36 weeks GA range T2-weighted MRI datasets. It includes 3D CNN-based intra-uterine localisation of the fetal trunk and landmark-guided pose estimation steps that allow automated DSVR reconstruction in the standard radiological space irrespective of the fetal trunk position or the regional stack coverage. The additional step for generation of the common template space and rejection of outliers provides the means for automated exclusion of stacks affected by low image quality or extreme motion. The pipeline was quantitatively evaluated on a series of experiments including fetal MRI datasets and simulated rotation motion. Furthermore, we performed a qualitative assessment of the image reconstruction quality in terms of the definition of vascular structures on 100 early (median 23.14 weeks) and late (median 31.79 weeks) GA group MRI datasets covering 21 to 36 weeks GA range.

Enhanced implicit function-based network for arbitrary-scale image super-resolution

With the great development of deep learning, the performance of single image super-resolution (SR) has achieved tremendous progress. As an emerging and promising branch of the SR task, the arbitrary-scale SR task is receiving increasing attention from researchers due to its efficiency and practicality. Although the recent work learning implicit image function opened a solution for arbitrary-scale image SR, its reconstructed images contained structural distortions caused by defective prediction of high-frequency textures. To overcome this problem and further improve the performance of arbitrary-scale image SR, we propose an effective arbitrary-scale SR network, namely, enhanced arbitrary-scale super-resolution, which achieves the arbitrary-scale SR task in a single model by introducing a local-global encoder and enhanced implicit image function. Unlike conventional SR methods, which only stack up convolutional blocks to extract the local feature, the local-global encoder has two branches in parallel, including the local feature branch and the global prior branch. The former effectively extracts the local feature from a low-resolution image and the latter extracts the global prior to assist in the high-resolution image reconstruction. Next, we redesigned an enhanced implicit image function in the form of a dual modulation multiplayer perceptron (MLP) by replacing the implicit image function with a vanilla MLP. Moreover, we introduce the spatial encoding to further reduce structural distortions of reconstructed images. Extensive experiments were conducted to evaluate the performance and demonstrate the superiority of our proposed model.

Application of High-Resolution Face Recognition and EDF Image Reconstruction in English Classroom Teaching

This study examines classroom monitoring in order to increase the effectiveness of English classroom instruction. This article uses high-resolution algorithms and EDF image reconstruction technology in the English classroom education system to improve the high resolution of students’ faces. This work presents a fast pixel capture function to enhance the rapid video capture function. The stack velocity picking of the speed spectrum is the most important aspect of velocity analysis. To create successful changes to the algorithm, this method often employs optimization algorithms as a way of stack velocity choosing speed. Finally, based on algorithm improvements, this study builds the system structure and develops simulation tests to validate system performance. The English classroom teaching system created in this work can properly determine the real-time status of students by facial recognition and has excellent user satisfaction, indicating that it may effectively increase the teaching impact, according to experimental research.

Prior information-based high-resolution tomography image reconstruction from a single digitally reconstructed radiograph

Tomography images are essential for clinical diagnosis and trauma surgery, allowing doctors to understand the internal information of patients in more detail. Since the large amount of x-ray radiation from the continuous imaging during the process of computed tomography scanning can cause serious harm to the human body, reconstructing tomographic images from sparse views becomes a potential solution to this problem. Here we present a deep-learning framework for tomography image reconstruction, namely TIReconNet, which defines image reconstruction as a data-driven supervised learning task that allows a mapping between the 2D projection view and the 3D volume to emerge from corpus. The proposed framework consists of four parts: feature extraction module, shape mapping module, volume generation module and super resolution module. The proposed framework combines 2D and 3D operations, which can generate high-resolution tomographic images with a relatively small amount of computing resources and maintain spatial information. The proposed method is verified on chest digitally reconstructed radiographs, and the reconstructed tomography images have achieved PSNR value of 18.621 ± 1.228 dB and SSIM value of 0.872 ± 0.041 when compared against the ground truth. In conclusion, an innovative convolutional neural network architecture is proposed and validated in this study, which proves that there is the potential to generate a 3D high-resolution tomographic image from a single 2D image using deep learning. This method may actively promote the application of reconstruction technology for radiation reduction, and further exploration of intraoperative guidance in trauma and orthopedics.

Label‐free single‐shot imaging with on‐axis phase‐shifting holographic reflectance quantitative phase microscopy

Quantitative phase microscopy (QPM) has been emerged as an indispensable diagnostic and characterization tool in biomedical imaging with its characteristic nature of label-free, noninvasive, and real time imaging modality. The integration of holography to the conventional microscopy opens new advancements in QPM featuring high-resolution and quantitative three-dimensional image reconstruction. However, the holography schemes suffer in space-bandwidth and time-bandwidth issues in the off-axis and phase-shifting configuration, respectively. Here, we introduce an on-axis phase-shifting holography based QPM system with single-shot imaging capability. The technique utilizes the Fizeau interferometry scheme in combination with polarization phase-shifting and space-division multiplexing to achieve the single-shot recording of the multiple phase-shifted holograms. Moreover, the high-speed imaging capability with instantaneous recording of spatially phase shifted holograms offers the flexible utilization of the approach in dynamic quantitative phase imaging with robust phase stability. We experimentally demonstrated the validity of the approach by quantitative phase imaging and depth-resolved imaging of paramecium cells. Furthermore, the technique is applied to the phase imaging and quantitative parameter estimation of red blood cells. This integration of a Fizeau-based phase-shifting scheme to the optical microscopy enables a simple and robust tool for the investigations of engineered and biological specimen with real-time quantitative analysis.

Static coded aperture in robotic X-ray tomography systems.

Coded aperture X-ray computed tomography is a computational imaging technique capable of reconstructing inner structures of an object from a reduced set of X-ray projection measurements. Coded apertures are placed in front of the X-ray sources from different views and thus significantly reduce the radiation dose. This paper introduces coded aperture X-ray computed tomography for robotic X-ray systems which offer positioning flexibility. While single coded-aperture 3D tomography was recently introduced for standard trajectory CT scanning, it is shown that significant gains in imaging performance can be attained by simple modifications in the CT scanning trajectories enabled by emerging dual robotic CT systems. In particular, the subject is fixed on a plane and the CT system uniformly rotates around the r -axis which is misaligned with the coordinate axes. A single stationary coded aperture is placed on front of the robotic X-ray source above the plane and the corresponding X-ray projections are measured by a two-dimensional detector on the second arm of the robotic system. The compressive measurements with misalignment enable the reconstruction of high-resolution three-dimensional volumetric images from the low-resolution coded projections on the detector at a sub-sampling rate. An efficient algorithm is proposed to generate the rotation matrix with two basic sub-matrices and thus the forward model is formulated. The stationary coded aperture is designed based on the Pearson product-moment correlation coefficient analysis and the direct binary search algorithm is used to obtain the optimized coded aperture. Simulations using simulated datasets show significant gains in reconstruction performance compared to conventional coded aperture CT systems.