Low Resolution Research Articles

Self-Supervised Deep Blind Video Super-Resolution.

Existing deep learning-based video super-resolution (SR) methods usually depend on the supervised learning approach, where the training data is usually generated by the blurring operation with known or predefined kernels (e.g., Bicubic kernel) followed by a decimation operation. However, this does not hold for real applications as the degradation process is complex and cannot be approximated by these idea cases well. Moreover, obtaining high-resolution (HR) videos and the corresponding low-resolution (LR) ones in real-world scenarios is difficult. To overcome these problems, we propose a self-supervised learning method to solve the blind video SR problem, which simultaneously estimates blur kernels and HR videos from the LR videos. As directly using LR videos as supervision usually leads to trivial solutions, we develop a simple and effective method to generate auxiliary paired data from original LR videos according to the image formation of video SR, so that the networks can be better constrained by the generated paired data for both blur kernel estimation and latent HR video restoration. In addition, we introduce an optical flow estimation module to exploit the information from adjacent frames for HR video restoration. Experiments show that our method performs favorably against state-of-the-art ones on benchmarks and real-world videos.

Use of a Multiscale Vision Transformer to Predict Nursing Activities Score From Low-Resolution Thermal Videos in an Intensive Care Unit

Use of a Multiscale Vision Transformer to Predict Nursing Activities Score From Low-Resolution Thermal Videos in an Intensive Care Unit

Multimodal identification of the mouse brain using simultaneous Ca 2+ imaging and fMRI.

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome- based identification to be successful and explored various features of these data.

Detail-preserving Joint Image Upsampling

Image operators can be instrumental to computational imaging and photography. However, many of them are computationally intensive. In this article, we propose an effective yet efficient joint upsampling method to accelerate various image operators. We show that edge-preserving filtering can be facilitated with a downsampling-and-upsampling process. Moreover, when the extent of smoothing is mild, the process is detail preserving, i.e., the fine details lost in the low-resolution (LR) images can be accurately restored in the high-resolution (HR) images. Given an HR input and an LR output of an operator, we downsample the HR input and calculate its affinities to the HR input. By applying the affinities to the LR output, we promote its resolution. Due to the strong detail-preserving property, the HR output derived in the previous step may exhibit aliasing artifacts around the salient edges. We further refine it based on the linear relations in a small neighborhood to rid the artifacts. Experiments on various image operators show that our method achieves superior quality over the state-of-the-art joint upsampling methods. Furthermore, the running time of our method is linear to the number of pixels. Our naive implementation derives 1080P images in real time (24 fps) on an NVIDIA GTX 3070 GPU.

Bias correction for phase-based cr-MREPT using low resolution B1+ magnitude

Objective Full-form Magnetic Resonance Electrical Properties Tomography (MREPT) requires both B1+ magnitude and phase information. Since B1+ phase can be obtained faster and with higher SNR compared to B1+ magnitude, several phase-based methods have been developed for conductivity imaging. However, phase-based methods suffer from a concave bias due to the assumption that ∇|B1+| is negligible in the ROI. Approach In this paper, we re-derive the central equation of phase-based cr-MREPT without assuming that ∇|B1+| is negligible and thus propose a correction method directly integrated into the equation system. Main results Proposed method successfully corrects the concave bias on both simulated and experimental data and significantly increases image quality. Significance The proposed correction method depends on a very low-resolution |B1+| map, and therefore the imaging time does not increase significantly for obtaining B1+ magnitude. Moreover, correction can be achieved using simulated B1+ magnitude, hence completely removing the additional imaging requirement.

Observation of Io's Resurfacing via Plume Deposition Using Ground‐Based Adaptive Optics at Visible Wavelengths With LBT SHARK‐VIS

AbstractSince volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io's surface have been monitored from both spacecraft and ground‐based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a ground‐based telescope. These images, acquired by the SHARK‐VIS instrument on the Large Binocular Telescope, show evidence of a major resurfacing event on Io's trailing hemisphere. When compared to the most recent spacecraft images, the SHARK‐VIS images show that a plume deposit from a powerful eruption at Pillan Patera has covered part of the long‐lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth‐based telescopes. The SHARK‐VIS instrument ushers in a new era of high resolution imaging of Io's surface using adaptive optics at visible wavelengths.

Hierarchical diversity partitioning of microscopic epibiont community on intertidal molluscan shells and inert surfaces over three geographic regions in Japan.

Microscopic epibionts on molluscan shells are a component of the biodiversity of intertidal coastal areas. Because molluscan shells are discrete habitats for the epibiont community, and the molluscan basibionts belong to the local community, epibiont diversity can be evaluated hierarchically by basibiont categories including species. To evaluate the structure of epibiont diversity and effects of taxonomic resolution on the evaluation, epibionts on molluscan shells and inert surfaces were investigated at three geographically distant sites in Japan. In total, 94 species-level taxonomic units of epibionts were obtained from 31 basibiont molluscan species and inert surfaces (plastics and rock chips). The density and the species richness at the site of the lowest latitude were significantly lower than those at the other sites. The epibiont community differed between the three sites, although the major portion of the epibionts were diatoms. Between-site diversity contributed most of the total diversity of the species richness and Simpson diversity in the five levels of the hierarchical partitioning: sample (individual basibiont), basibiont species (molluscan species), surface group (bivalves, chitons + limpets, and globose gastropods), site, and the total. The taxonomic resolution did not markedly affect the variability of communities between the three sites, although the taxon richness was reduced to 51 in the genus-level analysis. The lower taxonomic resolution (genus level); however, increased the contribution of the within-sample and decreased the contribution of β diversities at the higher hierarchies, leading to a possible overestimation of biotic homogenization between the communities.

Computed chest radiography for total body irradiation: image quality and clinical feasibility

Objective. In myeloablative total body irradiation (TBI), lung shielding blocks are used to reduce the dose to the lungs and hence decrease the risk of radiation pneumonitis. Some centers are still using mega-Volt (MV) imaging with dedicated silver halide-based films during simulation and treatment for lung delineation and position verification. However, the availability of these films has recently become an issue. This study examines the clinical performance of a computed radiography (CR) solution in comparison to radiographic films and potential improvement of image quality by filtering and post-processing. Approach. We compared BaFBrI-based CR plates to radiographic films. First, images of an aluminum block were analyzed to assess filter impact on scatter reduction. Secondly, a dedicated image quality phantom was used to assess signal linearity, signal-to-noise ratio (SNR), contrast and spatial resolution. Ultimately, a clinical performance study involving two impartial observers was conducted on an anthropomorphic chest phantom, employing visual grading analysis (VGA). Various filter materials and positions as well as post-processing were examined, and the workflow between CR and film was compared. Main results. CR images exhibited high SNR and linearity but demonstrated lower spatial and contrast resolution when compared to film. However, filtering improved contrast resolution and SNR, while positioning filters inside the cassette additionally enhanced sharpness. Image processing improved VGA scores, while additional filtering also resulted in higher spine visibility scores. CR shortened TBI simulation by over 10 minutes for one patient, alongside a dose reduction by order of 0.1 Gy. Significance. This study highlights potential advantages of shifting from conventional radiographic film to CR for TBI. Overall, CR with the incorporation of processing and filtering proves to be suitable for TBI chest imaging. When compared to radiographic film, CR offers advantages such as reduced simulation time and dose delivery, re-usability of image plates and digital workflow integration.

FQ-UWF: Unpaired Generative Image Enhancement for Fundus Quality Ultra-Widefield Retinal Images.

Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy and retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular degeneration. Conventional supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truths. However, obtaining such refined paired ground truths is a formidable challenge. To tackle this issue, we propose an unpaired, degradation-aware, super-resolution technique for enhancing UWF retinal images. Our approach leverages recent advancements in deep learning: specifically, by employing generative adversarial networks and attention mechanisms. Notably, our method excels at enhancing and super-resolving UWF images without relying on paired, clean ground truths. Through extensive experimentation and evaluation, we demonstrate that our approach not only produces visually pleasing results but also establishes state-of-the-art performance in enhancing and super-resolving UWF retinal images. We anticipate that our method will contribute to improving the accuracy of clinical assessments and treatments, ultimately leading to better patient outcomes.

Development of a Novel Non-invasive Metabolomics Assay to Predict Implantation Potential of Human Embryos.

Can a set of metabolites present in embryo culture media correlate with embryo implantation? Case-control study in two phases: discovery phase (101 samples) and validation phase (169 samples), collected between 2018 and 2022, with a total of 218 participants. Culture media samples with known implantation outcomes were collected after blastocyst embryo transfer (including both PGT and non-PGT cycles) and were analyzed using chromatography followed by mass spectrometry. The spectra were processed and analyzed using statistical and machine learning techniques to identify biomarkers associated with embryo implantation, and to develop a predictive model. In the discovery phase, 148 embryo implantation biomarkers were identified using high resolution equipment, and 47 of them were characterized. Our results indicate a significant enrichment of tryptophan metabolism, arginine and proline metabolism, and lysine degradation biochemical pathways. After transferring the method to a lower resolution equipment, a model able to assign a Metabolite Pregnancy Index (MPI) to each embryo culture media was developed, taking the concentration of 36 biomarkers as input. Applying this model to 20% of the validation samples (N=34) used as the test set, an accuracy of 85.29% was achieved, with a PPV (Positive Predictive Value) of 88% and a NPV (Negative Predictive Value) of 77.78%. Additionally, informative results were obtained for all the analyzed samples. Metabolite concentration in the media after in vitro culture shows correlation with embryo implantation potential. Furthermore, the mathematical combination of biomarker concentrations using Artificial Intelligence techniques can be used to predict embryo implantation outcome with an accuracy of around 85%.

GM-DETR: Research on a Defect Detection Method Based on Improved DETR.

Defect detection is an indispensable part of the industrial intelligence process. The introduction of the DETR model marked the successful application of a transformer for defect detection, achieving true end-to-end detection. However, due to the complexity of defective backgrounds, low resolutions can lead to a lack of image detail control and slow convergence of the DETR model. To address these issues, we proposed a defect detection method based on an improved DETR model, called the GM-DETR. We optimized the DETR model by integrating GAM global attention with CNN feature extraction and matching features. This optimization process reduces the defect information diffusion and enhances the global feature interaction, improving the neural network's performance and ability to recognize target defects in complex backgrounds. Next, to filter out unnecessary model parameters, we proposed a layer pruning strategy to optimize the decoding layer, thereby reducing the model's parameter count. In addition, to address the issue of poor sensitivity of the original loss function to small differences in defect targets, we replaced the L1 loss in the original loss function with MSE loss to accelerate the network's convergence speed and improve the model's recognition accuracy. We conducted experiments on a dataset of road pothole defects to further validate the effectiveness of the GM-DETR model. The results demonstrate that the improved model exhibits better performance, with an increase in average precision of 4.9% (mAP@0.5), while reducing the parameter count by 12.9%.

Cross-Modality Reference and Feature Mutual-Projection for 3D Brain MRI Image Super-Resolution.

High-resolution (HR) magnetic resonance imaging (MRI) can reveal rich anatomical structures for clinical diagnoses. However, due to hardware and signal-to-noise ratio limitations, MRI images are often collected with low resolution (LR) which is not conducive to diagnosing and analyzing clinical diseases. Recently, deep learning super-resolution (SR) methods have demonstrated great potential in enhancing the resolution of MRI images; however, most of them did not take the cross-modality and internal priors of MR seriously, which hinders the SR performance. In this paper, we propose a cross-modality reference and feature mutual-projection (CRFM) method to enhance the spatial resolution of brain MRI images. Specifically, we feed the gradients of HR MRI images from referenced imaging modality into the SR network to transform true clear textures to LR feature maps. Meanwhile, we design a plug-in feature mutual-projection (FMP) method to capture the cross-scale dependency and cross-modality similarity details of MRI images. Finally, we fuse all feature maps with parallel attentions to produce and refine the HR features adaptively. Extensive experiments on MRI images in the image domain and k-space show that our CRFM method outperforms existing state-of-the-art MRI SR methods.

3D simulations of a neon burning convective shell in a massive star

ABSTRACT The treatment of convection remains a major weakness in the modelling of stellar evolution with one-dimensional (1D) codes. The ever-increasing computing power makes now possible to simulate in three-dimensional (3D) part of a star for a fraction of its life, allowing us to study the full complexity of convective zones with hydrodynamics codes. Here, we performed state-of-the-art hydrodynamics simulations of turbulence in a neon-burning convective zone, during the late stage of the life of a massive star. We produced a set of simulations varying the resolution of the computing domain (from 1283 to 10243 cells) and the efficiency of the nuclear reactions (by boosting the energy generation rate from nominal to a factor of 1000). We analysed our results by the mean of Fourier transform of the velocity field, and mean-field decomposition of the various transport equations. Our results are in line with previous studies, showing that the behaviour of the bulk of the convective zone is already well captured at a relatively low resolution (2563), while the details of the convective boundaries require higher resolutions. The different boosting factors used show how various quantities (velocity, buoyancy, abundances, and abundance variances) depend on the energy generation rate. We found that for low boosting factors, convective zones are well mixed, validating the approach usually used in 1D stellar evolution codes. However, when nuclear burning and turbulent transport occur on the same time-scale, a more sophisticated treatment would be needed. This is typically the case when shell mergers occur.

Meta-learning based blind image super-resolution approach to different degradations

Although recent studies on blind single image super-resolution (SISR) have achieved significant success, most of them typically require supervised training on synthetic low resolution (LR)-high resolution (HR) paired images. This leads to re-training necessity for different degradations and restricted applications in real-world scenarios with unfavorable inputs. In this paper, we propose an unsupervised blind SISR method with input underlying different degradations, named different degradations blind super-resolution (DDSR). It formulates a Gaussian modeling on blur degradation and employs a meta-learning framework for solving different image degradations. Specifically, a neural network-based kernel generator is optimized by learning from random kernel samples, referred to as random kernel learning. This operation provides effective initialization for blur degradation optimization. At the same time, a meta-learning framework is proposed to resolve multiple degradation modelings on the basis of alternative optimization between blur degradation and image restoration, respectively. Differing from the pre-trained deep-learning methods, the proposed DDSR is implemented in a plug-and-play manner, and is capable of restoring HR image from unfavorable LR input with degradations such as partial coverage, noise addition, and darkening. Extensive simulations illustrate the superior performance of the proposed DDSR approach compared to the state-of-the-arts on public datasets with comparable memory load and time consumption, yet exhibiting better application flexibility and convenience, and significantly better generalization ability towards multiple degradations. Our code is available at https://github.com/XYLGroup/DDSR.

Using magnetic resonance-guided laser interstitial thermal therapy corpus callosotomy to lateralize a seizure focus for staged surgical approach

New onset refractory status epilepticus (NORSE) is an etiologically heterogeneous condition that is associated with high morbidity and mortality. NORSE is often refractory to medical management prompting a workup for epilepsy surgery. Because NORSE remains etiologically elusive in most cases, surgical evaluations are challenging, especially when the epileptogenic zone (EZ) is not easy to lateralize as can be seen in frontal lobe seizures. Lateralizing a frontal lobe EZ may be challenging due to bilateral synchrony from commissural connections through the corpus callosum and low spatiotemporal resolution of the scalp electroencephalography (EEG). We report a pediatric patient with NORSE presenting with focal impaired awareness seizures clustering into super refractory status epilepticus (SRSE). She required surgical intervention for the treatment of her seizures after failing therapeutic doses of antiseizure medications, anesthetic drips, immunomodulation with methylprednisolone, intravenous immunoglobulin and anakinra, and the ketogenic diet. Despite her semiology being focal, the seizures were not well lateralized on scalp EEG and during phase 2 stereo-EEG (sEEG). Anterior magnetic resonance-guided laser interstitial thermal therapy corpus callosotomy (MRgLITT CC) was performed in a multistage surgical approach to successfully lateralize the EZ with a left-lateralized ictal pattern seen after reimplantation of sEEG electrodes. Our case suggests that minimally invasive MRgLITT CC can be successfully used to lateralize an EZ in frontal lobe epilepsy and that epilepsy surgery should be considered in patients with NORSE with SRSE. We also demonstrate that laser interstitial thermal therapy (LITT), while not always resulting in seizure freedom, can sufficiently disrupt a network to abort status epilepticus and lead to seizure improvements.

A Pilot Clinical and Technical Validation of an Immersive Virtual Reality Platform for 3D Anatomical Modeling and Contouring in Support of Surgical and Radiation Oncology Treatment Planning.

The aim of this study was to validate a novel medical virtual reality (VR) platform used for medical image segmentation and contouring in radiation oncology and 3D anatomical modeling and simulation for planning medical interventions, including surgery. The first step of the validation was to verify quantitatively and qualitatively that the VR platform can produce substantially equivalent 3D anatomical models, image contours, and measurements to those generated with existing commercial platforms. To achieve this, a total of eight image sets and 18 structures were segmented using both VR and reference commercial platforms. The image sets were chosen to cover a broad range of scanner manufacturers, modalities, and voxel dimensions. The second step consisted of evaluating whether the VR platform could provide efficiency improvements for target delineation in radiation oncology planning. To assess this, the image sets for five pediatric patients with resected standard-risk medulloblastoma were used to contour target volumes in support of treatment planning of craniospinal irradiation, requiring complete inclusion of the entire cerebral-spinal volume. Structures generated in the VR and the commercial platforms were found to have a high degree of similarity, with dicesimilarity coefficient ranging from 0.963 to 0.985 for high-resolution images and 0.920 to 0.990 for lower resolution images. Volume, cross-sectional area, and length measurements were also found to be in agreement with reference values derived from a commercial system, with length measurements having a maximum difference of 0.22mm, angle measurements having a maximum difference of 0.04°, and cross-sectional area measurements having a maximum difference of 0.16 mm2. The VR platform was also found to yield significant efficiency improvements, reducing the time required to delineate complex cranial and spinal target volumes by an average of 50% or 29min.

Spatial domain noise removal filtering for low-resolution digital images

<p>n this research work, six different filters are applied on a low resolution 8 b/pixel gray-scale images, which operate on small sub-images (windows of 3×3 to 11×11 pixels). The enhanced images are used to compare the efficiency of the different six filters using the peak signal to noise ratio (PSNR) image quality measure. Noise peak elimination filter (PSNR)=36.63) outperforms others, such as median filter (PSNR=36.61), while corruption estimation (PSNR=36.03) significantly cuts processing time by only processing the corrupted pixels while maintaining image details. Mean filter (PSNR=34.05) is sensitive to outliers, which cause the image's sharpness and fine features to be lost. By avoiding averaging across edges, bimodal-averaging filter (PSNR=35.30), which improves on the mean filter, chooses the mean of the biggest population. The median-mean filtering (PSNR=36.32), which combines median and mean filters and determines the output pixel by averaging the median and some nearby pixels, is another improvement above averaging.</p>

TQU-HG dataset and comparative study for hand gesture recognition of RGB-based images using deep learning

Hand gesture recognition has great applications in human-computer interaction (HCI), human-robot interaction (HRI), and supporting the deaf and mute. To build a hand gesture recognition model using deep learning (DL) with high results then needs to be trained on many data and in many different conditions and contexts. In this paper, we publish the TQU-HG dataset of large RGB images with low resolution (640×480) pixels, low light conditions, and fast speed (16 fps). TQU-HG dataset includes 60,000 images collected from 20 people (10 male, 10 female) with 15 gestures of both left and right hands. A comparative study with two branches: i) based on Mediapipe TML and ii) Based on convolutional neural networks (CNNs) (you only look once (YOLO); YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-Nas, single shot multiBox detector (SSD) VGG16, residual network (ResNet)18, ResNext50, ResNet152, ResNext50, MobileNet V3 small, and MobileNet V3 large), the architecture and operation of CNNs models are also introduced in detail. We especially fine-tune the model and evaluate it on TQU-HG and HaGRID datasets. The quantitative results of the training and testing are presented (F1-score of YOLOv8, YOLO-Nas, MobileNet V3 small, ResNet50 is 98.99%, 98.98%, 99.27%, 99.36%, respectively on the TQU-HG dataset and is 99.21%, 99.37%, 99.36%, 86.4%, 98.3%, respectively on the HaGRID dataset). The computation time of YOLOv8 is 6.19 fps on the CPU and 18.28 fps on the GPU.

Early animal management in northern Europe: multi-proxy evidence from Swifterbant, the Netherlands

The nature and timing of the transition to farming north of the Linearbandkeramik zone in Europe is the subject of much debate, but our understanding of this fundamental shift in lifeways is hampered by the low resolution of available data. This article presents new multi-proxy evidence from Swifterbant (4240–4050 BC), in the Dutch wetlands, for morphologically domestic cattle with two different dietary regimes. The authors argue that the results indicate early animal management, alongside arable farming and the continuance of foraging practices, prompting the reconsideration away from broad statements about the Neolithic north of the Linearbandkeramik zone towards more local trajectories.

Geo-spatial epidemiology of gallbladder cancer in Bihar, India

Recently, a substantial increase in gallbladder cancer (GBC) cases has been reported in Bihar, India. The region's groundwater can naturally contain harmful concentrations of arsenic, which appears to be epidemiologically linked to the unusually high incidence. However, the root causes remain largely unexplored. Recent findings of uranium in the state's groundwater may also have associations. This study investigates the geo-spatial epidemiology of GBC in Bihar, India—with a focus on the correlation between environmental carcinogens, particularly arsenic and uranium in groundwater, and the incidence of GBC. Utilizing data from 8460 GBC patients' registration records over an 11-year period at a single health center, the research employs Semi-parametric Geographically Weighted Poisson Regression (S-GWPR) to account for non-stationarity associations and explores significant factors contributing to GBC prevalence at a subdistrict level. The S-GWPR model outperformed the standard Poisson regression model. The estimates suggest that arsenic and uranium concentrations in groundwater did not present significant associations; however, this could be due to the lower resolution of this data at the district level, necessitating higher resolution data for accurate estimates. Other socio-environmental factors included demonstrated significant regional heterogeneity in their association with GBC prevalence. Notably, each 1 % increase in the coverage of well- and canal-irrigated areas is associated with a maximum of 3.0 % and 5.2 % rise in the GBC incidence rate, respectively, likely attributable to carcinogen exposure from irrigation water. Moreover, distance to the health center and domestic electricity connections appear to influence the number of reported GBC cases. The latter suggests that access to electricity might have facilitated the use of groundwater pumps—increasing exposure to carcinogens. The results underscore the necessity for targeted health policies and interventions based on fine-resolution spatial analysis, as well as ongoing environmental monitoring and research to better understand the multifaceted risk factors contributing to GBC.