High-resolution Imaging Research Articles

The condensation of HP1α/Swi6 imparts nuclear stiffness

Biomolecular condensates have emerged as major drivers of cellular organization. It remains largely unexplored, however, whether these condensates can impart mechanical function(s) to the cell. The heterochromatin protein HP1α (Swi6 in Schizosaccharomyces pombe) crosslinks histone H3K9 methylated nucleosomes and has been proposed to undergo condensation to drive the liquid-like clustering of heterochromatin domains. Here, we leverage the genetically tractable S.pombe model and a separation-of-function allele to elucidate a mechanical function imparted by Swi6 condensation. Using single-molecule imaging, force spectroscopy, and high-resolution live-cell imaging, we show that Swi6 is critical for nuclear resistance to external force. Strikingly, it is the condensed yet dynamic pool of Swi6, rather than the chromatin-bound molecules, that is essential to imparting mechanical stiffness. Our findings suggest that Swi6 condensates embedded in the chromatin meshwork establish the emergent mechanical behavior of the nucleus as a whole, revealing that biomolecular condensation can influence organelle and cell mechanics.

Unsupervised Test-Time Adaptation Learning for Effective Hyperspectral Image Super-Resolution With Unknown Degeneration.

Fusing a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multi-spectral image has provided an effective way for HSI super-resolution (SR). The key lies on inferring the posteriori of the latent (i.e., HR) HSI using an appropriate image prior and the likelihood determined by the degeneration between the latent HSI and the observed images. However, in scenarios with complex imaging environments and various imaging scenes, the prior of HSIs can be prohibitively complicated and the degeneration is often unknown, which causes it difficult to accurately infer the posteriori of each latent HSI. To tackle this problem, we present an unsupervised test-time adaptation learning (UTAL) framework for HSI SR under unknown degeneration. Instead of directly modeling the complicated image prior, it first implicitly learns a content-agnostic prior shared across different images through supervisedly pre-training a mutual-guiding fusion module on extensive synthetic data. Then, it adapts the shared prior to those private characteristics in the latent HSI for posteriori inference through unsupervisedly learning a self-guiding adaptation module and a degeneration estimation network on two observed images in the test phase. Such a two-stage learning scheme models the complicated image prior in a divide-and-conquer manner, which eases the modeling difficulty and improves the prior accuracy. Moreover, the unknown degeneration can be estimated properly. Both of these two advantages empower us to accurately infer the posteriori of the latent HSI, thereby increasing the generalization performance in real applications. Additionally, in order to further mitigate the over-fitting in coping with more challenging cases (e.g., degenerations in both spectral and spatial domains are unknown) and speed up, we propose to meta-train UTAL on extensive synthetic SR tasks and solve it using an alternative optimization strategy such that UTAL learns to produce good generalization performance in real challenging cases with a small number of gradient descent steps. To verify the efficacy of UTAL, we evaluate it on HSI SR tasks with different unknown degenerations as well as some other HSI restoration tasks (e.g., compressive sensing), and report strong results superior to that of existing competitors.

Dual-beam X-ray nano-holotomography.

Nanotomography with hard X-rays is a widely used technique for high-resolution imaging, providing insights into the structure and composition of various materials. In recent years, tomographic approaches based on simultaneous illuminations of the same sample region from different angles by multiple beams have been developed at micrometre image resolution. Transferring these techniques to the nanoscale is challenging due to the loss in photon flux by focusing the X-ray beam. We present an approach for multi-beam nanotomography using a dual-beam Fresnel zone plate (dFZP) in a near-field holography setup. The dFZP generates two nano-focused beams that overlap in the sample plane, enabling the simultaneous acquisition of two projections from slightly different angles. This first proof-of-principle implementation of the dual-beam setup allows for the efficient removal of ring artifacts and noise using machine-learning approaches. The results open new possibilities for full-field multi-beam nanotomography and pave the way for future advancements in fast holotomography and artifact-reduction techniques.

ReLAP-Net: Residual Learning and Attention Based Parallel Network for Hyperspectral and Multispectral Image Fusion

Remote sensing applications require high-resolution images to obtain precise information about the Earth???s surface. Multispectral images have high spatial resolution but low spectral resolution. Hyperspectral images have high spectral resolution but low spatial resolution. This study proposes a residual learning and attention-based parallel network based on residual network and channel attention. The network performs image fusion of a high spatial resolution multispectral image and a low spatial resolution hyperspectral image. The network training and fusion experiments are conducted on four public benchmark data sets to show the effectiveness of the proposed model. The fusion performance is compared with classical signal processing???based image fusion techniques. Four image metrics are used for the quantitative evaluation of the fused images. The proposed network improved fusion ability by reducing the root mean square error and relative dimensionless global error in synthesis and increased the peak signal-to-noise ratio when compared to other state-of-the-art models.

Deep learning models and methods for solving the problems of remote monitoring of forest resources

Deep learning models and methods for solving the problems of remote monitoring of forest resources

High-contrast multi-surface imaging of latent fingerprints using color-tunable YOF:Tb3+,Eu3+ ultrafine nanophosphors with high quantum yield.

Visualization of latent fingerprints (LFPs) using conventional powders has faced challenges on multicolor surfaces. However, these challenges are addressed by the advent of fluorescent powders in LFP detection, and they have redefined the effectiveness of the powder dusting method. In this study, color-tunable YOF:Tb3+,Eu3+ nanophosphors were examined for LFP recognition and were evaluated for their practicality on different types of surfaces. Under 254 nm UV irradiation, the LFPs developed using these nanophosphors showed clear and distinct ridge patterns with level 1, 2, and 3 details. The ultrafine particles of these nanophosphors adhered to the ridge patterns and replicated the minutiae of the LFPs. Meanwhile, the variation of the Tb3+/Eu3+ ratio demonstrated multicolor fluorescence emission from the nanophosphors, which provided better contrast between the ridge patterns on complex surfaces. Furthermore, the high luminescence quantum yield of the nanophosphors ensured high-resolution fluorescence images of the LFPs with a well-defined pattern that was recognizable even without any microscope or sophisticated instrumentation.

Efficient Guided Grad-CAM Tuned Patch Neural Network for Accurate Anomaly Detection in Full Images

Deep learning-based anomaly detection in images has recently gained popularity as an investigative field with many global submissions. To simplify complex data analysis, researchers in the deep learning subfield of machine learning employ Artificial Neural Networks (ANNs) with many hidden layers. Finding data occurrences that significantly differ from generalizable to most data sets is the primary goal of anomaly detection. Many medical imaging applications use convolutional neural networks (CNNs) to examine anomalies automatically. While CNN structures are reliable feature extractors, they encounter challenges when simultaneously classifying and segmenting spots that need removal from scans. We suggest a separate and integration system to solve these issues, separated into two distinct departments: classification and segmentation. Initially, many network architecturesare taught independently for each abnormality, and these networks’ main components are combined. A sharedcomponent of the branched structure functions for all abnormalities. The final structure has two branches: onehas distinct sub-networks, each intended to classify a particular abnormality, and the other for segmenting various abnormalities. Deep CNNs training directly on high-resolution images necessitate input layer image compression, which results in the loss of information necessary for detecting medical abnormalities. A guided GradCAM (GCAM) tuned patch neural network is applied to full-size images for anomaly localization. Therefore, the suggested approach merges the pre-trained deep CNNs with class activation mappings and area suggestion systems to construct abnormality sensors and then fine-tunes the CNNs on picture patches, focusing on medical abnormalities instead of training on whole images. A mammogram data set was used to test the deep patch classifier, which had a 99% overall classification accuracy. A Brain tumor image data set was used to test the integrateddetector’s ability to detect abnormalities, and it did so with an average precision of 0.99.

Overtone photothermal microscopy for high-resolution and high-sensitivity vibrational imaging.

Photothermal microscopy is a highly sensitive pump-probe method for mapping nanostructures and molecules through the detection of local thermal gradients. While visible photothermal microscopy and mid-infrared photothermal microscopy techniques have been developed, they possess inherent limitations. These techniques either lack chemical specificity or encounter significant light attenuation caused by water absorption. Here, we present an overtone photothermal (OPT) microscopy technique that offers high chemical specificity, detection sensitivity, and spatial resolution by employing a visible probe for local heat detection in the C-H overtone region. We demonstrate its capability for high-fidelity chemical imaging of polymer nanostructures, depth-resolved intracellular chemical mapping of cancer cells, and imaging of multicellular C. elegans organisms and highly scattering brain tissues. By bridging the gap between visible and mid-infrared photothermal microscopy, OPT establishes a new modality for high-resolution and high-sensitivity chemical imaging. This advancement complements large-scale shortwave infrared imaging approaches, facilitating multiscale structural and chemical investigations of materials and biological metabolism.

Forcing conditions of cross-shelf plumes on a wide continental shelf, Winyah Bay, South Atlantic Bight

Buoyant cross-shelf river plumes can extend far offshore through the combined effect of buoyancy and wind forcing, creating a critical land-ocean link in global biogeochemical cycles. On the Carolinas continental shelf, cross-shelf plume structure has been analyzed using satellite imagery, with forcing conditions represented by an estuarine Richardson number, wind stress, and alongshore pressure gradient. Three distinct cross-shelf plume patterns emerged, each occurring under an upwelling-favorable wind: (1) The separated plume, when a single filament of buoyant water spreads offshore (a prototypical cross-shelf plume structure); (2) The upwind-curving plume, which turns against the wind at some offshore distance and is created by stronger buoyancy forcing; and (3) The multi-lobe plume, which is partially trapped by the coast with multiple streaks protruding offshore and is created by stronger wind forcing, and further aided by a coincident alongshore pressure gradient force. The latter two regimes represent a low-wind, high discharge limit and a strong-wind limit of cross-shelf plumes. High-resolution satellite images reveal rich submesoscale variability associated with each plume type. Results suggest plume transport extends farthest offshore in low-energy separated plumes through a balance of weak buoyancy and weak wind forcing.

Salt tectonics synchronous with salt deposition in the Santos Basin (Ariri Formation, Brazil)

Halokinetic deformations synchronous with salt deposition are processes already suggested in several salt giants including the Lower Cretaceous salt deposits of the Santos Basin in Brazil. However, the dynamics of syn-depositional deformation has never been studied in a coherent depositional and structural framework. This study investigates well data and high-resolution 3D seismic images in the evaporitic Ariri Formation of the Santos Basin to understand the intra-salt deformations, estimate their timing and establish a link with the depositional setting of the evaporites. The seismic data shows numerous examples of intra-salt onlaps, erosive surfaces and sedimentary wedges highlighting the occurrence of deformations during salt deposition. Two dominant processes have initiated salt creep: the density contrasts between a dense upper anhydrite-rich unit and a less dense lower halite-dominated unit and the gravitational system promoted by the late-rift subsidence. Updip, extension prevailed during salt deposition and favored a boudinage of the Ariri Formation. Downdip, early contraction favored the development of well-developed intra-salt minibasins. Deformation occurring during salt deposition and induced topographic relief likely influenced the hydrological conditions. It probably led to intra-salt dissolution/reprecipitation processes and hyper-saline conditions favoring the precipitation of tachyhydrite deposits in localized intra-salt depocenters. Eventually, post-salt minibasins systematically developed above syn-salt depocenters, indicating that syn-salt deposition deformation had a strong impact on post-salt evolution. This new understanding of the intra-salt deformation in the Santos Basin paves the way for new interpretations of halokinetic deformations reported elsewhere in the South Atlantic or in other salt-giant provinces.

LSDN Empowers the Metaverse Communication to Achieve High-Resolution Visuals with Lower Bandwidth

Deploying super-resolution models on metaverse terminal devices can enhance visual effects without increasing network bandwidth. However, deploying most current super-resolution networks on metaverse terminal devices with limited hardware resources poses a challenge due to their large volumes and high computing power consumption. In this paper, we present a lightweight separation and distillation network (LSDN) aimed at reducing the model complexity by prioritizing network structure. Specifically, we initially present the blueprint separable convolution (BSConv) to decrease model complexity and utilize the BSConv and information distillation mechanism building the channel separation distillation block (CSDB). Subsequently, we develop the enhanced spatial attention block (ESA) and Fused-MBConv (FMBConv) to explore latent information. In addition, we employ three CSDBs, an ESA, and an FMBConv to construct the residual attention unit (RAU). Finally, we concatenate several RAUs and amalgamate their hierarchical results, and transmit them to the upsampler for reconstructing the high-resolution images. We carried out comprehensive experiments on a range of datasets and found conclusive evidence that the LSDN outperforms state-of-the-art approaches, exhibiting notable enhancements in quantitative and qualitative terms.

MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images.

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. Algorithmic comparative assessments and blind evaluations conducted by 10 board-certified radiologists indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines and airways. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.

High-Resolution Imaging Study on Photodissociation of OCS+ [A2ΠΩ=1/2,3/2 (ν1 0 ν3)

The development of the velocity map ion imaging (VMI) technique has greatly advanced the study of photodissociation dynamics. The high-resolution imaging study of the photodissociation allows for the acquisition of precise and detailed information on the fragments. This information can further provide more insight into the energy partition and potential pathways involved in the photodissociation process. In this study, we report the investigation on the photodissociation of OCS+ via the A2ΠΩ=1/2,3/2 states following the excitation of A2Π (ν1 0 ν3) ← X2Π (0 0 0) by using time-sliced VMI techniques in the ultraviolet region. Our investigation revealed significant mode-dependent recoil anisotropies and branching ratios of two product channels for both Ω = 1/2 and Ω = 3/2. The photolysis products also exhibited dramatic deviation in angular distributions and generally comparable kinetic energy distributions following the excitation to the same vibrational modes of A2ΠΩ states with two separate spin-orbit components. According to the observation in this study and previously reported photodissociation mechanisms of the OCS+ cations, the decay from the A2Π3/2 state was more likely via the internal conversion to high rovibrational states of the X2Π state, in comparison to the A2Π1/2 state.

Extraction of offshore aquaculture areas from Gaofen-2 remote sensing imagery based on deep learning

ABSTRACT Extracting offshore aquaculture areas from high-resolution remote sensing images is significant for rational planning and dynamic management of aquaculture. However, the complex and changeable characteristics of aquaculture areas will lead to difficulty during high-resolution image processing. In this paper, an improved U-Net deep learning model is proposed to extract offshore aquaculture areas from high-resolution images. This model is improved by the pyramid pooling module (PPM) and up-sampling structure based on the original U-Net model. PPM and the up-sampling structure can better mine semantic and position information in high-resolution images, reduce the interference and adhesion of information in images, and improve the extraction accuracy of unclear aquaculture areas. Based on Gaofen-2 remote sensing image data of Rongcheng Bay in the eastern waters of Weihai City, Shandong Province, the extraction of offshore aquaculture areas is studied. The results show that the F1 score and MIoU of the proposed network model are 93.63% and 87.93%, respectively. Compared with several commonly used deep learning models, the improved U-Net model has a better extraction effect.

(089) CORRELATIONS BETWEEN IMMUNOHISTOCHEMICAL STAINING AND PAIN-RELATED ASSESSMENTS IN PATIENTS WITH NEUROPROLIFERATIVE VESTIBULODYNIA

Abstract Introduction Mast cell accumulation and nerve proliferation has been hypothesized to lead to pain symptoms for patients with neuroproliferative vestibulodynia (NPV). A clinically suspected diagnosis of NPV can be confirmed by examining immunohistochemical (IHC) staining of excised vestibular specimens. Compared to controls, specimens from NPV patients have been found to contain >8 mast cells per high-power field (HPF) and increased density of nerves. Objective To assess for correlations between patient reported pain-related measures and computer-assisted histometry of excised vestibulectomy specimen IHC staining with CD117 (marker for mast cells) and PGP9.5 (marker for nerves) in a cohort of histopathologically confirmed NPV patients. Methods Patients (n=65) with lifelong or acquired NPV undergoing vestibulectomy had IHC-staining of excised vestibular tissue. Tissue sections from the 1:00-11:00 and/or 12:00 regions of the vestibule were examined and high-resolution images were captured using a microscope at 100x and 200x magnification. A minimum of 2 photomicrographs per magnification were taken for each tissue section that included epithelial basement membrane and adjacent subepithelium representative of the majority of immunostained tissue. Fractional area of positive immunostaining of all photomicrographs was determined using computer-assisted histometry by ImageJ. Mean fractional area was determined from 3 separate measurements of each photomicrograph. Correlations were made between density of IHC staining and the following assessments: pain domain of the Female Sexual Function Index (FSFI), the Short Form McGill Pain Questionnaire (SF-MPQ), and cotton-tipped swab testing. We also examined correlations with the Patient Global Impression of Improvement (PGI-I), a treatment response index and indirect assessment of pain. Data were analyzed using Spearman correlations and the Mann-Whitney test. Results The study cohort had a median age of 26 (IQR = 23-31) years and experienced symptoms for a median duration of 11 (IQR = 6-15) years. While all patients reported high levels of pain (median FSFI pain domain score = 0.0), there were no correlations between CD117- or PGP9.5-immunopositive mean fractional area and FSFI pain domain or SF-MPQ scores. There was no difference in immunopositive mean fractional area between lifelong and acquired NPV. A positive correlation between fractional area of CD117 immunostaining in the 1:00-11:00 region of the vestibule and cotton-tipped swab test pain score approached but did not reach statistical significance (r = 0.24, p = 0.063). No correlations were observed between immunostaining and PGI-I or cotton-tipped swab testing scores. Likewise, SF-MPQ domain scores had no correlation to PGI-I or cotton-tipped swab testing. Median cotton-tipped swab testing pain scores were significantly higher (p < 0.001) in patients with lifelong NPV (7.6) compared to those with acquired NPV (5.7). Conclusions Our findings suggest that the high levels of pain experienced by NPV patients over a prolonged period of time may be dissociated from the density of mast cells and nerves in vestibular tissue. We postulate that any potential threshold of mast cell accumulation and increased nerve density that results in symptoms of vestibulodynia has been exceeded in our patient cohort. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Dare Bioscience; Pfizer; Prometheus Laboratories; Sprout Pharmaceuticals.

Multi kernel cross sparse graph attention convolutional neural network for brain magnetic resonance imaging super-resolution

High-resolution Magnetic Resonance Imaging (MRI) is pivotal in both diagnosing and treating brain tumors, assisting physicians in diagnosis and treatment by displaying anatomical structures. Utilizing convolutional neural network-based super-resolution methods enables the efficient acquisition of high-resolution MRI images.However, Convolutional neural networks are limited by their kernel size, which restricts their ability to capture a wider field of view, potentially leading to feature omission and difficulties in establishing global and local feature relationships. To overcome these shortcomings, We have designed a novel network architecture that highlights three main modules: (i) Multiple Convolutional Feature (MCF)extraction module, which diversifies convolution operations for extracting image features, achieving comprehensive feature representation. (ii) Multiple Groupss of Cross-Iterative Feature(MGCIF) modules, promoting inter-channel feature interactions and emphasizing crucial features needed for subsequent learning. (iii) A Graph Neural Network Module based on a sparse attention mechanism, capable of connecting distant pixel features and identifying influential neighboring pixels for target pixel inpainting. To evaluate the accuracy of our proposed network, we conducted tests on four datasets, comprising two sets of brain tumor data and two sets of healthy head MRI data, all of which underwent varying degrees of degradation. We conducted experiments using nineteen super-resolution (SR) models. Our experiments were carried out on four datasets, and the results demonstrate that our method outperforms the current leading-edge methods. In the four datasets, our model showed improvements in Peak Signal-to-Noise Ratio (PSNR) scores compared to the second-place model,with increase of 1.16 %,1.08 %,0.19 % and 0.53 % for ×2,and 2.26 %,1.67 %,0.13 % and 0.45 % for × 4,respectively.

Enhancing higher-order modal response in multifrequency atomic force microscopy with a coupled cantilever system.

Multifrequency atomic force microscopy (AFM) utilizes the multimode operation of cantilevers to achieve rapid high-resolution imaging and extract multiple properties. However, the higher-order modal response of traditional rectangular cantilever is weaker in air, which affects the sensitivity of multifrequency AFM detection. To address this issue, we previously proposed a bridge/cantilever coupled system model to enhance the higher-order modal response of the cantilever. This model is simpler and less costly than other enhancement methods, making it easier to be widely used. However, previous studies were limited to theoretical analysis and preliminary simulations regarding ideal conditions. In this paper, we undertake a more comprehensive investigation of the coupled system, taking into account the influence of probe and excitation surface sizes on the modal response. To facilitate the exploration of the effectiveness and optimal conditions for the coupled system in practical applications, a macroscale experimental platform is established. By conducting finite element analysis and experiments, we compare the performance of the coupled system with that of traditional cantilevers and quantify the enhancement in higher-order modal response. Also, the optimal conditions for the enhancement of macroscale cantilever modal response are explored. Additionally, we also supplement the characteristics of this model, including increasing the modal frequency of the original cantilever and generating additional resonance peaks, demonstrating the significant potential of the coupled system in various fields of AFM.

Susceptibility Mapping of Thaw Slumps Based on Neural Network Methods along the Qinghai–Tibet Engineering Corridor

Climate warming has induced the thawing of permafrost, which increases the probability of thaw slump occurrences in permafrost regions of the Qinghai–Tibet Engineering Corridor (QTEC). As a key and important corridor, thaw slump distribution is widespread, but research into effectively using neural networks to predict thaw slumping remains insufficient. This study automated the identification of thaw slumps within the QTEC and investigated their environmental factors and susceptibility assessment. We applied a deep learning-based semantic segmentation method, combining U-Net with ResNet101, to high spatial and temporal resolution images captured by the Gaofen-1 images. This methodology enabled the automatic delineation of 455 thaw slumps within the corridor area, covering 40,800 km², with corresponding precision, recall, and F1 scores of 0.864, 0.847, and 0.856, respectively. Subsequently, employing a radial basis function neural network model on this inventory of thaw slumps, we investigated environmental factors that could precipitate the occurrence of thaw slumps and generated sensitivity maps of thaw slumps along the QTEC. The model demonstrated high accuracy, and the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve reached 0.95. The findings of the study indicate that these thaw slumps are predominantly located on slopes with gradients of 1–18°, distributed across mid-elevation regions ranging from 4500 to 5500 m above sea level. Temperature and precipitation were identified as the predominant factors that influenced the distribution of thaw slumps. Approximately 30.75% of the QTEC area was found to fall within high to extremely high susceptibility zones. Moreover, validation processes confirmed that 82.75% of the thaw slump distribution was located within areas of high or higher sensitivity within the QTEC.

Compressed sensing photoacoustic imaging based on the gradient projection Newton pursuit algorithm

Photoacoustic imaging (PAI) is a unique non-invasive biomedical imaging technique that illuminates biological tissue with short-pulsed laser light. This process generates acoustic waves, which in turn enable high-resolution and high-contrast imaging of deep tissue. However, the complexity of PAI imaging data presents challenges in achieving rapid and optimal reconstruction results due to the time and resource demands of traditional Nyquist sampling. On the contrary, compressive sensing (CS) overcomes the limitations of Nyquist sampling by recovering the original signal with a lower amount of sampled data, resulting in significant cost savings in signal transmission and processing. Additionally, CS-based hard-thresholding iterative algorithms possess various advantages in sparse optimization, including sparsity control and fast computation. Moreover, the assumptions of the Gradient Projection Newton Pursuit (GPNP) for CS problems are relatively weaker compared to many state-of-the-art algorithms. Therefore, we investigated the performance of the GPNP algorithm in PAI. We compared our algorithm with improved iterative hard thresholding (IIHT), conjugate gradient iterative hard thresholding (CGIHT), compressive sampling matching pursuit (CoSaMP), SPGL1, SALSA and elastic net algorithms using phantom, Vasculature, and SGERF simulation images. The simulation results demonstrate that the GPNP algorithm achieves excellent image reconstruction performance when evaluating both the quality of reconstruction and the reconstruction time. Taking the simulation results of Vasculature with a noise level of 40 dB and 40 channels as an example, the PNSR index of GPNP has improved by 4.09, 11.03, 12.89, 13.14, 13.15, and 11.75 compared to IIHT, CGIHT, elastic net algorithms, SPGL1, SALSA and CoSaMP, respectively.

Design of A prototype 128 × 128 ROIC array for 2.6 μm-wavelength SWIR image sensor applications

This paper presents the development and evaluation of a 128 × 128 Readout Integrated Circuit (ROIC) prototype, engineered for Short-Wave Infrared (SWIR) imaging at a specific target wavelength of 2.6 μm. Employing silicon-level verification, this work undertook an exhaustive analysis of the ROIC's performance, identifying key areas for enhancement to improve SWIR imaging systems. Fabricated with 0.18-μm CMOS technology, the ROIC is tailored for integration with Indium Gallium Arsenide (InGaAs) Focal Plane Arrays (FPAs), facilitating high-resolution imaging. The prototype consumes 42.25 mW of power and achieves a frame rate of 390 frames per second. The fabricated chip show that the random noise level is 72.65 μVrms and Pixel-FPN is 21 LSBrms. This investigation lays a critical groundwork for future SWIR imaging advancements, providing valuable insights and methodologies to boost imaging performance in various applications.