High-quality Reconstruction Research Articles

Robust holographic imaging for real-world applications with joint optimization

Digital inline holography offers a compact, lensless imaging solution, but its practical deployment is often hindered by the need for precise system alignment and calibration, particularly regarding propagation distance. This work presents J-Net, a robust, untrained neural network that significantly mitigates these limitations. J-Net eliminates the need for prior knowledge or calibration of the propagation distance by simultaneously reconstructing both the complex-valued object magnitude and the propagation distance from a single hologram. This inherent robustness to distance variations makes J-Net highly practical for real-world applications where precise system control is difficult or impossible. Experimental results demonstrate high-quality amplitude and phase reconstruction even under mismatched distance conditions, showcasing J-Net’s potential to enable robust deployment of holographic imaging across diverse fields.

3D-HoloNet: fast, unfiltered, 3D hologram generation with camera-calibrated network learning

Computational holographic displays typically rely on time-consuming iterative computer-generated holographic (CGH) algorithms and bulky physical filters to attain high-quality reconstruction images. This trade-off between inference speed and image quality becomes more pronounced when aiming to realize 3D holographic imagery. This work presents 3D-HoloNet, a deep neural network-empowered CGH algorithm for generating phase-only holograms (POHs) of 3D scenes, represented as RGB-D images, in real time. The proposed scheme incorporates a learned, camera-calibrated wave propagation model and a phase regularization prior into its optimization. This unique combination allows for accommodating practical, unfiltered holographic display setups that may be corrupted by various hardware imperfections. Results tested on an unfiltered holographic display reveal that the proposed 3D-HoloNet can achieve 30 fps at full HD for one color channel using a consumer-level GPU while maintaining image quality comparable to iterative methods across multiple focused distances.

3D visualization of human colon tissue using a modified CUBIC-based tissue-clearing technique

Background: Colorectal cancer represents one of the most common neoplastic diseases worldwide, making it a frequent focus in routine pathological analyses. Visualizing complex three-dimensional (3D) structures, such as nerves within tumors, requires thick tissue sections, which necessitates the use of optical tissue-clearing methods to achieve transparency. However, following tissue clearing, samples typically require advanced imaging techniques such as light-sheet and two-photon confocal microscopy, which are usually unavailable in standard histological laboratories. Objective: We aimed to demonstrate how a well-established tissue-clearing approach can be adapted for use in a routine histological laboratory, enabling a robust 3D visualization of nerve fibers in samples of both normal human colon and colon cancer tissues. Methods: We modified the “clear unobstructed brain/body imaging cocktails” method, originally developed for whole-brain imaging in mice, and applied it to human colon tissue samples measuring approximately 10 mm3, a standard size typically processed in pathological laboratories. Results: Our protocol, which integrates a tissue-clearing technique, enabled reliable immunofluorescent visualization of colonic nerve fibers labeled with anti-β3-tubulin antibodies. The labeled nerve fibers could be observed using a standard epifluorescence microscope, and high-quality 3D reconstructions were generated through a simple image analysis approach using the open-source software ilastik, which eliminates the need for confocal microscopy. Conclusion: The proposed steps provide a valuable method for researchers to visualize complex 3D structures, such as neural cells and processes, in both normal and tumor-transformed tissue settings.

Image Compression–Encryption Algorithm Based on 3D Discrete Chaotic System and Josephus Circle

Numerous image encryption schemes are hindered by weak correlations with plaintex images, inadequate image reconstruction quality, and inefficient transmission and storage. To address these problems, this paper proposes an image compression–encryption algorithm that combines a three-dimensional discrete chaotic system, Josephus circle and compression perception. First, a new three-dimensional discrete hyperchaotic system is constructed, utilizing a hash function to generate the initial value of the chaotic system, through which the measurement matrix and the sequences required for the encryption process are constructed. An encryption algorithm based on chaotic sequence and Josephus circle is designed. Finally, the proposed compressed encryption algorithm is simulated and tested, and the results of experimental tests indicate that the proposed algorithm provides robust security and high-quality reconstruction performance.

An efficient and high-quality scheme for cone-beam CT reconstruction from sparse-view dat

Computed tomography (CT) is capable of generating detailed cross-sectional images of the scanned objects non-destructively. So far, CT has become an increasingly vital tool for 3D modelling of cultural relics. Compressed sensing (CS)-based CT reconstruction algorithms, such as the algebraic reconstruction technique (ART) regularized by total variation (TV), enable accurate reconstructions from sparse-view data, which consequently reduces both scanning time and costs. However, the implementation of the ART-TV is considerably slow, particularly in cone-beam reconstruction. In this paper, we propose an efficient and high-quality scheme for cone-beam CT reconstruction based on the traditional ART-TV algorithm. Our scheme employs Joseph's projection method for the computation of the system matrix. By exploiting the geometric symmetry of the cone-beam rays, we are able to compute the weight coefficients of the system matrix for two symmetric rays simultaneously. We then employ multi-threading technology to speed up the reconstruction of ART, and utilize graphics processing units (GPUs) to accelerate the TV minimization. Experimental results demonstrate that, for a typical reconstruction of a 512 × 512 × 512 volume from 60 views of 512 × 512 projection images, our scheme achieves a speedup of 14 × compared to a single-threaded CPU implementation. Furthermore, high-quality reconstructions of ART-TV are obtained by using Joseph's projection compared with that using traditional Siddon's projection.

Approaching maximum resolution in structured illumination microscopy via accurate noise modeling

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

Weamba: Weather-Degraded Remote Sensing Image Restoration with Multi-Router State Space Model

Adverse weather conditions, such as haze and raindrop, consistently degrade the quality of remote sensing images and affect subsequent vision-based applications. Recent years have witnessed advancements in convolutional neural networks (CNNs) and Transformers in the field of remote sensing image restoration. However, these methods either suffer from limited receptive fields or incur quadratic computational overhead, leading to an imbalance between performance and model efficiency. In this paper, we propose an effective vision state space model (called Weamba) for remote sensing image restoration by modeling long-range pixel dependencies with linear complexity. Specifically, we develop a local-enhanced state space module to better aggregate rich local and global information, both of which are complementary and beneficial for high-quality image reconstruction. Furthermore, we design a multi-router scanning strategy for spatially varying feature extraction, alleviating the issue of redundant information caused by repeated scanning directions in existing methods. Extensive experiments on multiple benchmarks show that the proposed Weamba performs favorably against state-of-the-art approaches.

Implicit neural representation-based method for metal-induced beam hardening artifact reduction in X-ray CT imaging.

In X-ray computed tomography (CT), metal-induced beam hardening artifacts arise from the complex interactions between polychromatic X-ray beams and metallic objects, leading to degraded image quality and impeding accurate diagnosis. A previously proposed metal-induced beam hardening correction (MBHC) method provides a theoretical framework for addressing nonlinear artifacts through mathematical analysis, with its effectiveness demonstrated by numerical simulations and phantom experiments. However, in practical applications, this method relies on precise segmentation of highly attenuating materials and parameter estimations, which limit its ability to fully correct artifacts caused by the intricate interactions between metals and other dense materials, such as bone orteeth. This study aims to develop a parameter-free MBHC method that eliminates the need for accurate segmentation and parameter estimations, thereby addressing the limitations of the original MBHCapproach. The proposed method employs implicit neural representations (INR) to generate two tomographic images: one representing the monochromatic attenuation distribution at a specific energy level, and another capturing the nonlinear beam hardening effects caused by the polychromatic nature of X-ray beams. A loss function drives the generation of these images, where the predicted projection data is nonlinearly modeled by the combination of the two images. This approach eliminates the need for geometric and parameter estimation of metals, providing a more generalizedsolution. Numerical and phantom experiments demonstrates that the proposed method effectively reduces beam hardening artifacts caused by interactions between highly attenuating materials such as metals, bone, and teeth. Additionally, the proposed INR-based method demonstrates potential in addressing challenges related to data insufficiencies, such as photon starvation and truncated fields of view in CTimaging. The proposed generalized MBHC method provides high-quality image reconstructions without requiring parameter estimations and segmentations, offering a robust solution for reducing metal-induced beam hardening artifacts in CT imaging.

Fast correction method for the translation position error of ptychography

Abstract Translation position errors in ptychography can significantly reduce the reconstruction quality. It is crucial to obtain the accurate translation positions between the probe and the object. Current methods for correcting the translation position error are applied into the iterative process of ptychography that will increase the computation time of a single iteration, and take much time to correct the translation position error and obtain a high reconstruction quality. A sub-pixel block matching method is proposed to ultrafast correct the translation position errors and greatly reduce the correction time. This method is applied in the preprocessing stage before iterations that does not increase the computational burden of a single iteration in ptychography and can quickly correct the translation position error. Simulations and experiments demonstrate that our method can significantly improve computational efficiency with ensuring correction accuracy.

Research on meshing method for industrial CT volume data based on iterative smooth signed distance surface reconstruction

Industrial Computed Tomography (CT) technology is increasingly applied in fields such as additive manufacturing and non-destructive testing, providing rich three-dimensional information for various fields, which is crucial for internal structure detection, defect detection, and product development. In subsequent processes such as analysis, simulation, and editing, three-dimensional volume data models often need to be converted into mesh models, making effective meshing of volume data essential for expanding the application scenarios and scope of industrial CT. However, the existing Marching Cubes algorithm has issues with low efficiency and poor mesh quality during the volume data meshing process. To overcome these limitations, this study proposes an innovative method for industrial CT volume data meshing based on the Iterative Smooth Signed Surface Distance (iSSD) algorithm. This method first refines the segmented voxel model, accurately extracts boundary voxels, and constructs a high-quality point cloud model. By randomly initializing the normals of the point cloud and iteratively updating the point cloud normals, the mesh is reconstructed using the SSD algorithm after each iteration update, ultimately achieving high-quality, watertight, and smooth mesh model reconstruction, ensuring the accuracy and reliability of the reconstructed mesh. Qualitative and quantitative analyses with other methods have further highlighted the excellent performance of the method proposed in this paper. This study not only improves the efficiency and quality of volume data meshing but also provides a solid foundation for subsequent three-dimensional analysis, simulation, and editing, and has important industrial application prospects and academic value.

RETRACTED: Ji et al. Temporal-Difference Graph-Based Optimization for High-Quality Reconstruction of MODIS NDVI Data. Remote Sens. 2024, 16, 2713

RETRACTED: Ji et al. Temporal-Difference Graph-Based Optimization for High-Quality Reconstruction of MODIS NDVI Data. Remote Sens. 2024, 16, 2713

Neural Radiance Fields for High-Fidelity Soft Tissue Reconstruction in Endoscopy.

The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft tissue regions by invalid pixels, deformations in soft tissue, and poor image quality, which severely limits their application in endoscopic scenarios. To address the above issues, we propose a novel framework to reconstruct high-fidelity soft tissue scenes from low-quality endoscopic images. We first construct an EndoTissue dataset of soft tissue regions in endoscopic images and fine-tune the Segment Anything Model (SAM) based on EndoTissue to obtain a potent segmentation network. Given a sequence of monocular endoscopic images, this segmentation network can quickly obtain the tissue mask images. Additionally, we incorporate tissue masks into a dynamic scene reconstruction method called Tensor4D to effectively guide the reconstruction of 3D deformable soft tissues. Finally, we propose adopting the image enhancement model EDAU-Net to improve the quality of the rendered views. The experimental results show that our method can effectively focus on the soft tissue regions in the image, achieving higher fidelity in detail and geometric structural integrity in reconstruction compared to state-of-the-art algorithms. Feedback from the user study indicates high participant scores for our method.

Reconstructing Tropical Cyclone Activity from Sedimentary Archives

The brevity of the instrumental record limits our knowledge of tropical cyclone activity on multidecadal to longer timescales and hampers our ability to diagnose climatic controls. Sedimentary archives containing event beds provide essential data on tropical cyclone activity over centuries and millennia. This review highlights the advantages and limitations of this approach and how these reconstructions have illuminated patterns of tropical cyclone activity and potential climate drivers over the last millennium. Key elements to developing high-quality reconstructions include confident attribution of event beds to tropical cyclones, assessing the potential role of other mechanisms, and evaluating the potential influence of geomorphic changes, sea-level variations, and sediment supply on a settings’ susceptibility to event bed deposition. Millennium-long histories of severe tropical cyclone occurrence are now available from many locations in the western North Atlantic and western North Pacific,revealing clear regional shifts in activity likely related to intervals of large-scale ocean-atmosphere reorganization. ▪ Prior to significant human influence in Earth's climate, natural climate variability dramatically altered patterns of tropical cyclone activity. ▪ For some regions (e.g., The Bahamas and the Marshall Islands), earlier intervals of tropical cyclone activity exceeded what humans have experienced during the recent period of instrumental measurements (∼1850 CE–present). ▪ Risk assessments based on the short instrumental record likely underestimate the threat posed by tropical cyclones in many regions. ▪ Changes in atmospheric and oceanic circulation associated with the Little Ice Age (∼1400–1800 CE) resulted in significant regional changes in tropical cyclone activity. ▪ Given the past sensitivity of tropical cyclone activity to climate change, we should anticipate regional shifts in tropical cyclone activity in response to ongoing anthropogenic warming of the planet.

Sequential Multimodal Underwater Single-Photon Lidar Adaptive Target Reconstruction Algorithm Based on Spatiotemporal Sequence Fusion

For the demand for long-range and high-resolution target reconstruction of slow-moving small underwater targets, research on single-photon lidar target reconstruction technology is being carried out. This paper reports the sequential multimodal underwater single-photon lidar adaptive target reconstruction algorithm based on spatiotemporal sequence fusion, which has strong information extraction and noise filtering ability and can reconstruct the target depth and reflective intensity information from complex echo photon time counts and spatial pixel relationships. The method consists of three steps: data preprocessing, sequence-optimized extreme value inference filtering, and collaborative variation strategy for image optimization to achieve high-quality target reconstruction in complex underwater environments. Simulation and test results show that the target reconstruction method outperforms the current imaging algorithms, and the built single-photon lidar system achieves underwater lateral and distance resolution of 5 mm and 2.5cm@6AL, respectively. This indicates that the method has a great advantage in sparse photon counting imaging and possesses the capability of underwater target imaging under the background of strong light noise. It also provides a good solution for underwater target imaging of small slow-moving targets with long-distance and high-resolution.

Selecting projection views based on error equidistribution for computed tomography

Background Nonuniform sampling is a useful technique to optimize the acquisition of projections with a limited budget. Existing methods for selecting important projection views have limitations, such as relying on blueprint images or excessive computing resources. Methods We aim to develop a simple nonuniform sampling method for selecting informative projection views suitable for practical CT applications. The proposed algorithm is inspired by two key observations: projection errors contain angle-specific information, and adding views around error peaks effectively reduces errors and improves reconstruction. Given a budget and an initial view set, the proposed method involves: estimating projection errors based on current set of projection views, adding more projection views based on error equidistribution to smooth out errors, and final image reconstruction based on the new set of projection views. This process can be recursive, and the initial view can be obtained uniformly or from a prior for greater efficiency. Results Comparison with popular view selection algorithms using simulated and real data demonstrates consistently superior performance in identifying optimal views and generating high-quality reconstructions. Notably, the new algorithm performs well in both PSNR and SSIM metrics while being computationally efficient, enhancing its practicality for CT optimization. Conclusions A projection view selection algorithm based on error equidistribution is proposed, offering superior reconstruction quality and efficiency over existing methods. It is ready for real CT applications to optimize dose utilization.

Compressive electron backscatter diffraction imaging.

Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete data set. The missing probe locations (or pixels in the image) are recovered via an inpainting process using a dictionary-learning based method called beta-process factor analysis (BPFA). To investigate the robustness of both our inpainting method and Hough-based indexing, we simulate subsampled and noisy EBSD data sets from a real fully sampled Ni-superalloy data set for different subsampling ratios of probe positions using both Gaussian and Poisson noise models. We find that zero solution pixel detection (inpainting un-indexed pixels) enables higher-quality reconstructions to be obtained. Numerical tests confirm high-quality reconstruction of band contrast and inverse pole figuremaps from only 10% of the probe positions, with the potential to reduce this to 5% if only inverse pole figuremaps are needed. These results show the potential application of this method in EBSD, allowing for faster analysis and extending the use of this technique to beam sensitive materials.

Robust broadband ptychography algorithms for high-harmonic soft X-ray supercontinua.

We demonstrate PaCMAN, a ptychography algorithm that can reconstruct high quality images with broadband illumination sources while being robust to shot, detector, and parasitic noise. We extend prior monochromatization work to improve accuracy, especially for discrete spectra, and also demonstrate how PaCMAN can be converted into Ms. PaCMAN, a multi-spectral variant that outperforms multi-spectral ePIE. When comparing speed-optimized ePIE and PaCMAN, we achieve high reconstruction quality in 4x less time, and when they are dose-optimized, we achieve high reconstruction quality in 1.5-2x less dose. These algorithms will enable rapid sub-10 nm imaging with table-top soft X-ray high harmonic supercontinua, advance attosecond coherent diffractive imaging, and reduce the dose needed to image radiation sensitive materials.

Filtered Backprojection Algorithms Enable Linear Scanning Computed Tomography High-Quality Reconstruction With Large Extended FOVs

Filtered Backprojection Algorithms Enable Linear Scanning Computed Tomography High-Quality Reconstruction With Large Extended FOVs

Rain removal method for single image of dual-branch joint network based on sparse transformer

In response to image degradation caused by rain during image acquisition, this paper proposes a rain removal method for single image of dual-branch joint network based on a sparse Transformer (DBSTNet). The developed model comprises a rain removal subnet and a background recovery subnet. The former extracts rain trace information utilizing a rain removal strategy, while the latter employs this information to restore background details. Furthermore, a U-shaped encoder-decoder branch (UEDB) focuses on local features to mitigate the impact of rainwater on background detail textures. UEDB incorporates a feature refinement unit to maximize the contribution of the channel attention mechanism in recovering local detail features. Additionally, since tokens with low relevance in the Transformer may influence image recovery, this study introduces a residual sparse Transformer branch (RSTB) to overcome the limitations of the Convolutional Neural Network’s (CNN’s) receptive field. Indeed, RSTB preserves the most valuable self-attention values for the aggregation of features, facilitating high-quality image reconstruction from a global perspective. Finally, the parallel dual-branch joint module, composed of RSTB and UEDB branches, effectively captures the local context and global structure, culminating in a clear background image. Experimental validation on synthetic and real datasets demonstrates that rain removal images exhibit richer detail information, significantly improving the overall visual effect.

Development of a low-dose strategy for propagation-based imaging helical computed tomography (PBI-HCT): high image quality and reduced radiation dose

Background. Propagation-based imaging computed tomography (PBI-CT) has been recently emerging for visualizing low-density materials due to its excellent image contrast and high resolution. Based on this, PBI-CT with a helical acquisition mode (PBI-HCT) offers superior imaging quality (e.g., fewer ring artifacts) and dose uniformity, making it ideal for biomedical imaging applications. However, the excessive radiation dose associated with high-resolution PBI-HCT may potentially harm objects or hosts being imaged, especially in live animal imaging, raising a great need to reduce radiation dose.Methods. In this study, we strategically integrated Sparse2Noise (a deep learning approach) with PBI-HCT imaging to reduce radiation dose without compromising image quality. Sparse2Noise uses paired low-dose noisy images with different photon fluxes and projection numbers for high-quality reconstruction via a convolutional neural network (CNN). Then, we examined the imaging quality and radiation dose of PBI-HCT imaging using Sparse2Noise, as compared to when Sparse2Noise was used in low-dose PBI-CT imaging (circular scanning mode). Furthermore, we conducted a comparison study on the use of Sparse2Noise versus two other state-of-the-art low-dose imaging algorithms (i.e., Noise2Noise and Noise2Inverse) for imaging low-density materials using PBI-HCT at equivalent dose levels.Results. Sparse2Noise allowed for a 90% dose reduction in PBI-HCT imaging while maintaining high image quality. As compared to PBI-CT imaging, the use of Sparse2Noise in PBI-HCT imaging shows more effective by reducing additional radiation dose (30%-36%). Furthermore, helical scanning mode also enhances the performance of existing low-dose algorithms (Noise2Noise and Noise2Inverse); nevertheless, Sparse2Noise shows significantly higher signal-to-noise ratio (SNR) value compared to Noise2Noise and Noise2Inverse at the same radiation dose level.Conclusions and significance. Our proposed low-dose imaging strategy Sparse2Noise can be effectively applied to PBI-HCT imaging technique and requires lower dose for acceptable quality imaging. This would represent a significant advance imaging for low-density materials imaging and for future live animals imaging applications.