Resolution Imaging Research Articles

MIGHTEE: The continuum survey data release 1

Abstract The MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) is one of the large survey projects using the MeerKAT telescope, covering four fields that have a wealth of ancillary data available. We present Data Release 1 of the MIGHTEE continuum survey, releasing total intensity images and catalogues over ∼20 deg2, across three fields at ∼1.2-1.3 GHz. This includes 4.2 deg2 over the Cosmic Evolution Survey (COSMOS) field, 14.4 deg2 over the XMM Large-Scale Structure (XMM-LSS) field and deeper imaging over 1.5 deg2 of the Extended Chandra Deep Field South (CDFS). We release images at both a lower resolution (7–9 arcsec) and higher resolution (∼5 arcsec). These images have central rms sensitivities of ∼1.3 −2.7 μJy beam−1 (∼1.2 −3.6 μJy beam−1) in the lower (higher) resolution images respectively. We also release catalogues comprised of ∼144 000 (∼114 000) sources using the lower (higher) resolution images. We compare the astrometry and flux-density calibration with the Early Science data in the COSMOS and XMM-LSS fields and previous radio observations in the CDFS field, finding broad agreement. Furthermore, we extend the source counts at the ∼10 μJy level to these larger areas (∼20 deg2) and, using the areal coverage of MIGHTEE we measure the sample variance for differing areas of sky. We find a typical sample variance of 10-20percnt for 0.3 and 0.5 sq. deg. sub-regions at S1.4 ≤ 200 μJy, which increases at brighter flux densities, given the lower source density and expected higher galaxy bias for these sources.

Analysis of Land Use and Land Cover Dynamics in the Highlands of Northern Ethiopia

In the highlands of northern Ethiopia, many land use and land cover changes (LULCC) have occurred during different activities of the populations. Despite its coverage and unwise use of natural resources, these land uses and land covers are facing increasing or decreasing their area coverage. Information derived from land use and land cover change detection is important to land conservation, sustainable development, and management of Natural resources. This purpose of this study is therefore concerned with identifying the change in land use and land cover detection of the Wejic watershed. To identify land cover changes detection; remote sensing data, satellite imagery and image processing techniques had done within two dates of 1998 and 2017 using Land sat TM 30 m resolution images using Arc GIS 10.1. Hence, a field survey was carried out in 2017 to identify the major land cover types in the watershed. Detection Analysis of land use and land cover dynamics were calculated from the selected watershed in Southern Tigray, Northern Ethiopia. These land use types were cultivated, forest, bush, shrub, and homestead lands. Furthermore, a normalized difference vegetation index (NDVI) was developed for both years. The land use and land cover change results indicated that forest, cultivated land, and bush land decreased by 4.4%, 2.2%, and 2.4%, respectively. However, shrub land and homestead increased by 5.5% and 3.6%, respectively. Therefore The change detection analysis using GIS and remote sensing could deliver useful information to understand the seasonal patterns of land use dynamics for planners and decision makers; consequently, sustainable land management planning is possible.

Smartphone Aided Funduscopy in Albino Rabbits

Aims: The methodology, utility and clinical feasibility of using a smartphone camera to obtain images of ocular fundus of albino rabbits with ocular hypertension and normotension were studied. A comparative analysis of fundus images obtained from two popularly used smartphone models, android and iOS device was performed on 36 adult albino rabbits. Study Design: A cross-sectional study involving two cohorts of ocular hypertensive and normotensive albino rabbits. Place and Duration of Study: Department of Veterinary Surgery and Radiology, College of Veterinary and Animal Sciences, Mannuthy for an eight-week period (2023). Methodology: A total of 36 adult albino rabbits (18 males and 18 females; age range 6- 8 months of age) with ocular normotensive eyes and unilateral ocular hypertension-induced models were used. The conscious animals were gently restrained in an acrylic restrainer. Instillation of topical mydriatic solution (tropicamide 0.8% and phenylephrine 5%) facilitated visual examination and enhanced field of view along with auxiliary condensing lenses of 20 and 40 Diopters. A comparison of fundus images obtained from two popularly used smartphone models, android and iOS devices was performed on 36 adult albino rabbits. Results: Fundus images comprising the optic nerve head and peripapillary retina were obtained with adequate spatial resolution and detail. The variation between optic disc of ocular hypertensive eye revealed mild edema and cupping of optic disc. Normal merangiotic retina and optic disc were clearly obtained in normotensive rabbit eyes. The iOS device used in this case yielded better images in resolution and field of view compared to the android version used in this case. Conclusion: Smart phone funduscopy offered good detail in contrast and resolution even in amelanotic eyes of albino rabbits using inbuilt technology without the need for an external adapter or sophisticated additional software. It is an animal-friendly futuristic solution for digital documentation, telemedicine, teaching and clinical prognosis formulation.

Super-resolution cropland mapping with Sentinel-2 images based on a self-training learning network

ABSTRACT The Sentinel-2 image is widely used for cropland mapping, but the limitation of its 10 m spatial resolution may significantly impact the accuracy of result in areas characterized by severe cropland fragmentation. To address the issue, this letter proposes a super-resolution cropland mapping model for Sentinel-2 images. The proposed model improves the spatial resolution of Sentinel-2 images to 2.5 m through self-training learning with the Swin Transformer for Image Restoration (SwinIR) model without needing fine-resolution training samples. Then, the random forest classification is applied to map cropland from the super-solved 2.5 m resolution image. The proposed model was assessed in the Jianghan Plain, China, and the results show that the super-resolution images produce cropland maps with higher accuracy than the original Sentinel-2 images. Comparing cropland mapping results before and after super-resolution, the overall accuracy improves from 0.82 to 0.89, while the commission error and omission error decrease from 0.09 to 0.05 and 0.17 to 0.11, respectively. This method addresses the shortage of training samples collection and improves the accuracy of cropland mapping, contributing to more reliable agricultural remote sensing applications.

1-arcsecond imaging of the ELAIS-N1 field at 144MHz using the LoTSS survey with the international LOFAR telescope

We present the first wide area ($2.5 deg ^2$) LOFAR high band antenna image at a resolution of $1.2 with a median noise of $ Jy beam $. It was made from an 8-hour International LOFAR Telescope (ILT) observation of the ELAIS-N1 field at frequencies ranging from 120 to 168\,MHz with the most up-to-date ILT imaging methods. This intermediate resolution falls between the highest possible resolution (0.3 achievable by using all ILT baselines and the standard 6-arcsecond resolution in the LOFAR Two-meter Sky Survey (LoTSS) image products utilising the LOFAR Dutch baselines only. This is the first demonstration of the feasibility of imaging using the ILT at a resolution of sim 1 which provides unique information on source morphology at scales that fall below the surface brightness limits at higher resolutions. The total calibration and imaging computational time is approximately 52,000 core hours, which is nearly five times more than required to produce a $6 resolution image. We also present a radio source catalogue containing 2263 sources detected over the $2.5 deg ^2$ image of the ELAIS-N1 field, with a peak intensity threshold of $5.5 The catalogue has been cross-matched with the LoTSS deep ELAIS-N1 field radio catalogue, and its flux density and positional accuracy have been investigated and corrected accordingly. We find that sim 80<!PCT!> of sources that we expect to be detectable based on their peak brightness in the LoTSS $6 resolution image are detected in this image, which is approximately a factor of two higher than for $0.3 resolution imaging in the Lockman Hole, implying there is a wealth of information on these intermediate scales.

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

AID-YOLO: An Efficient and Lightweight Network Method for Small Target Detector in Aerial Images

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the specific challenges relating to small object detection in aerial images and proposes an improved YOLOv8s-based detector named Aerial Images Detector-YOLO(AID-YOLO). Specifically, this study adopts the General Efficient Layer Aggregation Network (GELAN) from YOLOv9 as a reference and designs a four-branch skip-layer connection and split operation module Re-parameterization-Net with Cross-Stage Partial CSP and Efficient Layer Aggregation Networks (RepNCSPELAN4) to achieve a lightweight network while capturing richer feature information. To fuse multi-scale features and focus more on the target detection regions, a new multi-channel feature extraction module named Convolutional Block Attention Module with Two Convolutions Efficient Layer Aggregation Net-works (C2FCBAM) is designed in the neck part of the network. In addition, to reduce the sensitivity to position bias of small objects, a new function, Normalized Weighted Distance Complete Intersection over Union (NWD-CIoU_Loss) weight adaptive loss function, was designed in this study. We evaluate the proposed AID-YOLO method through ablation experiments and comparisons with other advanced models on the VEDAI (512, 1024) and DOTAv1.0 datasets. The results show that compared to the Yolov8s baseline model, AID-YOLO improves the mAP@0.5 metric by 7.36% on the VEDAI dataset. Simultaneously, the parameters are reduced by 31.7%, achieving a good balance between accuracy and parameter quantity. The Average Precision (AP) for small objects has improved by 8.9% compared to the baseline model (YOLOv8s), making it one of the top performers among all compared models. Furthermore, the FPS metric is also well-suited for real-time detection in aerial image scenarios. The AID-YOLO method also demonstrates excellent performance on infrared images in the VEDAI1024 (IR) dataset, with a 2.9% improvement in the mAP@0.5 metric. We further validate the superior detection and generalization performance of AID-YOLO in multi-modal and multi-task scenarios through comparisons with other methods on different resolution images, SODA-A and the DOTAv1.0 datasets. In summary, the results of this study confirm that the AID-YOLO method significantly improves model detection performance while maintaining a reduced number of parameters, making it applicable to practical engineering tasks in aerial image object detection.

Comparison Utilization of UAV Images & Satellite Images for Geospatial Analysis

The rapid advancements in remote sensing technologies have opened new avenues for geospatial analysis by providing high-quality imagery and data. Unmanned Aerial Vehicles (UAVs) and satellites have emerged as prominent tools for capturing Earth's surface information, each offering unique advantages and limitations. This paper presents a comprehensive comparison of the utilisation of UAV images and satellite images in geospatial analysis. The practical implications of employing UAVs and satellites in diverse scenarios include environmental monitoring, disaster management, urban planning, and agriculture. The benefits of UAVs, such as their ability to capture high- resolution images with precision and on-demand deployment, are contrasted with satellites' global coverage and consistent monitoring capabilities. Integrating UAV and satellite data is emphasized as a complementary approach to geospatial analysis. Techniques for fusing data from these sources are discussed, enabling the creation of comprehensive datasets that combine detailed local information from UAVs with broader context from satellite imagery.

Research on an SICM Scanning Image Resolution Enhancement Algorithm.

Scanning ion conductance microscopy (SICM) enables the non-invasive three-dimensional imaging of live cells and other structures in physiological environments. However, when imaging complex samples, SICM faces challenges such as having a low temporal resolution during slow scanning and a reduced signal-to-noise ratio during fast scanning, making it difficult to simultaneously improve both temporal and spatial resolution. To address these issues, this paper proposes an algorithm for enhancing image resolution under high-speed scanning. Firstly, scanning images are preprocessed using a median filtering algorithm to remove the salt-and-pepper noise generated during high-speed scanning. Next, the Canny edge detection algorithm is employed to extract the edges of the image targets. To avoid blurring the edges, the new edge-directed interpolation (NEDI) algorithm is then used to fill the edges, while non-edge areas are filled using bilinear interpolation, thereby enhancing the image resolution. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to analyze the imaging of articular chondrocytes. The results show that under a scanning speed of 480 nm/ms, the proposed algorithm improves the temporal resolution of imaging by 60% compared to traditional 2× resolution imaging, increases the peak signal-to-noise ratio of the scanning images by 7 dB, and achieves a structural similarity of 0.97. Therefore, the proposed algorithm effectively removes noise during high-speed scanning and improves the SICM scanning imaging resolution, thereby avoiding the reduction in temporal resolution when scanning larger resolution samples and effectively enhancing the performance of SICM scanning imaging.

Cascade operation-enhanced high-resolution representation learning for meticulous segmentation of bridge cracks

High-resolution (HR) crack images have proven valuable for bridge inspection using unmanned aerial vehicles (UAVs), offering fine details crucial for accurate segmentation. Traditional deep learning (DL) struggles with HR images due to downsampling issues and limited computational resources. To address this, we propose Cascade-FcaHRNet, a HR representation learning-based multiscale architecture. It incorporates a frequency-channel attention mechanism to capture tiny crack features, a two-stage cascade operation for global and local refinement, and a region-sensitive loss to avoid ambiguous predictions. Ablation studies confirm the effectiveness of these modifications. Robustness experiments show improvements in performance metrics for crack segmentation. In a field test, the Cascade-FcaHRNet accurately segments bridge cracks wider than 0.5 mm from 4 K resolution images, enhancing safety and efficiency in UAV-based bridge inspection. The approach holds potential for developing scientifically sound maintenance and management strategies.

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Superresolution (SR) aims to increase the resolution of images by recovering detail. Compared to standard interpolation, deep learning-based approaches learn features and their relationships to leverage prior knowledge of what low-resolution patterns look like in higher resolution. Deep neural networks can also perform image cross-calibration by learning the systematic properties of the target images. While SR for natural images aims to create perceptually convincing results, SR of scientific data requires careful quantitative evaluation. In this work, we demonstrate that deep learning can increase the resolution and calibrate solar imagers belonging to different instrumental generations. We convert solar magnetic field images taken by the Michelson Doppler Imager (resolution ∼2″ pixel−1; space based) and the Global Oscillation Network Group (resolution ∼2.″5 pixel−1; ground based) to the characteristics of the Helioseismic and Magnetic Imager (resolution ∼0.″5 pixel−1; space based). We also establish a set of performance measurements to benchmark deep-learning-based SR and calibration for scientific applications.

AI IMAGE GENERATOR THROUGH TEXT PROMT

In the era of digital content creation, the Text-to- Image Generator has emerged as a powerful and innovative tool that transforms textual descriptions into captivating visual representations. This website leverages advanced deep learning techniques to bridge the gap between imagination and reality, enabling users to effortlessly generate stunning images from their written words. Our Text-to-Image Generator website provides a user- friendly interface where individuals can input textual prompts, whether they are vivid descriptions, imaginative stories, or abstract concepts. Behind the scenes, state-of-the-art generative models decode the text and generate high-quality, contextually relevant images that reflect the essence of the input. In the digital landscape, the fusion of text and images has become a quintessential form of communication. Text-to-image generators serve as pivotal tools in this synergy, bridging the conceptual realm of language with the visual spectrum. This abstract delves into the conceptualization of a cutting-edge Text Prompt to Image Generator website, epitomizing the amalgamation of natural language processing and computer vision technologies. This innovative platform harnesses the power of advanced deep learning algorithms, empowering users to input textual descriptions and witness them materialize into vivid, high- resolution images. The generator employs state-of-the-art techniques such as Generative Adversarial Networks (GANs) and Transformer architectures to interpret nuanced textual cues, capturing abstract ideas and intricate details. Users can explore a diverse array of categories, from nature and architecture to fantastical realms, ensuring a versatile and captivating user experience. Key Words: "Text to Image Generator" , "AI Text to Image", "Text-Driven Image Creation" , "Generate Images from Text" , "Image Generator from Words".

LOFAR HBA observations of the Euclid Deep Field North (EDFN)

We present the first deep (72 h of observations) radio image of the Euclid Deep Field North (EDFN) obtained with the LOw-Frequency ARray (LOFAR) High Band Antenna (HBA) at 144 MHz. The EDFN is the latest addition to the LOFAR Two-Metre Sky Survey (LoTSS) Deep Fields, and these observations represent the first data release for this field. The observations produced a 6″ resolution image with a central rms noise of 32 μJy beam−1. A catalogue of ~23 000 radio sources above a signal-to-noise ratio threshold of five is extracted from the inner circular 10 deg2 region. We discuss the data analysis, and we provide a detailed description of how we derived the catalogue of radio sources, the issues related to direction-dependent calibration, and their effects on the final products. Finally, we derive the radio source counts at 144 MHz in the EDFN using catalogues of mock radio sources to derive the completeness correction factors. The source counts in the EDFN are consistent with those obtained from the first data release of the other LoTSS Deep Fields (ELAIS-N1, Lockman Hole and Bootes), despite the different method adopted to construct the final catalogue and to assess its completeness.

Modeling finer microstructure of random heterogeneous porous materials by fusing 2D high- and 3D low- resolution images

Modeling the finer microstructure is crucial for studying the microscopic and macroscopic characteristics of random heterogeneous porous materials. Multi-scale reconstruction is a feasible way to model finer microstructures. However, most of the traditional methods focus on reconstructing a single type of low-level features, while the neural network-based methods are heavily dependent on the dataset. This paper proposes a novel multi-scale reconstruction framework based on Neural Network-based Transform Mapping (NNTM) to modeling the finer microstructure. The number of network layer and input noise scale are determined by the pixel-size ratio between LRI and HRI. This allows the network to fit the corresponding transformation sub-mapping at different feature scales, thus effectively and accurately fusing the multi-scale information of 2D HRI and 3D LRI. More notably, without the training on a large dataset, and providing only a 2D HRI and a 3D LRI of the same sample, NNTM enables the modeling of high-resolution finer microstructure. Experiments were conducted on rock materials with different pore morphologies, and the morphological characteristics and physical properties were compared to the original high-resolution structure. The qualitative and quantitative evaluation criteria indicate a high consistency between the reconstructed finer microstructure and the original structure.

Dual-Task Network for Terrace and Ridge Extraction: Automatic Terrace Extraction via Multi-Task Learning

Terrace detection and ridge extraction from high-resolution remote sensing imagery are crucial for soil conservation and grain production on sloping land. Traditional methods use low-to-medium resolution images, missing detailed features and lacking automation. Terrace detection and ridge extraction are closely linked, with each influencing the other’s outcomes. However, most studies address these tasks separately, overlooking their interdependence. This research introduces a cutting-edge, multi-scale, and multi-task deep learning framework, termed DTRE-Net, designed for comprehensive terrace information extraction. This framework bridges the gap between terrace detection and ridge extraction, executing them concurrently. The network incorporates residual networks, multi-scale fusion modules, and multi-scale residual correction modules to enhance the model’s robustness in feature extraction. Comprehensive evaluations against other deep learning-based semantic segmentation methods using GF-2 terraced imagery from two distinct areas were undertaken. The results revealed intersection over union (IoU) values of 85.18% and 86.09% for different terrace morphologies and 59.79% and 73.65% for ridges. Simultaneously, we have confirmed that the connectivity of results is improved when employing multi-task learning for ridge extraction compared to directly extracting ridges. These outcomes underscore DTRE-Net’s superior capability in the automation of terrace and ridge extraction relative to alternative techniques.

Early remodeling and loss of light-induced dilation of retinal small arteries in CADASIL

A major hurdle to therapeutic development in cerebral small vessel diseases is the lack of in-vivo method that can be used repeatedly for evaluating directly cerebral microvessels. We hypothesised that Adaptive Optics (AO), which allows resolution images up to 1–2 μm/pixel at retinal level, could provide a biomarker for monitoring vascular changes in CADASIL, a genetic form of such condition. In 98 patients and 35 healthy individuals, the wall to lumen ratio (WLR), outer and inner diameter, wall thickness and wall cross-sectional area were measured in a parapapillary and/or paramacular retinal artery. The ratio of vessel diameters before and after light flicker stimulations was also calculated to measure vasoreactivity (VR). Multivariate mixed-model analysis showed that WLR was increased and associated with a larger wall thickness and smaller internal diameter of retinal arteries in patients. The difference was maximal at the youngest age and gradually reduced with aging. Average VR in patients was less than half of that of controls since the youngest age. Any robust association was found with clinical or imaging manifestations of the disease. Thus, AO enables the detection of early functional or structural vascular alterations in CADASIL but with no obvious link to the clinical or imaging severity.

Preliminary Evaluation of Geometric Positioning Accuracy of C-SAR Images Based on Automatic Corner Reflectors

C-SAR/01 and C-SAR/02 serve as successors to the GF-3 satellite. They are designed to operate in tandem with GF-3, collectively forming a C-band synthetic aperture radar (SAR) satellite constellation. This constellation aims to achieve 1 m resolution imaging with a revisit rate of one day. It can effectively cater to various applications such as marine disaster prevention, monitoring marine dynamic environments, and supporting marine scientific research, disaster mitigation, environmental protection, and agriculture. Geometric correction plays a pivotal role in acquiring highly precise geographic location data for ground targets. The geometric positioning accuracy without control points signifies the SAR satellite’s geometric performance. However, SAR images do not exhibit a straightforward image-point–object-point correspondence, unlike optical images. In this study, we introduce a novel approach employing high-precision automatic trihedral corner reflectors as ground control points (GCPs) to assess the geometric positioning accuracy of SAR images. A series of satellite-ground synchronization experiments was conducted at the Xilinhot SAR satellite calibration and validation site to evaluate the geometric positioning accuracy of different C-SAR image modes. Firstly, we calculated the azimuth and elevation angles of the corner reflectors based on satellite orbit parameters. During satellite transit, these corner reflectors were automatically adjusted to align with the radar-looking direction. We subsequently measured the exact longitude and latitude coordinates of the corner reflector vertex in situ using a high-precision real-time kinematics instrument. Next, we computed the theoretical image coordinates of the corner reflectors using the rational polynomial coefficients (RPC) model. After that, we determined the accurate position of the corner reflector in the Single Look Complex (SLC) SAR image using FFT interpolation and the sliding window method. Finally, we evaluated and validated the geometric positioning accuracy of C-SAR images by comparing the two coordinates. The preliminary results indicate that the positioning accuracy varies based on the satellite, imaging modes, and orbital directions. Nevertheless, for most sample points, the range positioning accuracy was better than 60 m, and the azimuth positioning accuracy was better than 80 m. These findings can serve as a valuable reference for subsequent applications of C-SAR satellites.

A Deep Learning-Based No-Reference Quality Metric for High-Definition Images Compressed With HEVC

An accurate no-reference image quality assessment metric for compression artifacts is essential for the broadcasting and streaming industries. Although we have witnessed impressive advances in the capturing, delivery and display technologies, we have not managed to match them with an accurate and perceptual based no-reference image quality metric. In this paper, we propose a unique perceptual based no-reference quality metric for compressed HD frames/images that is based on the DenseNet network architecture. We focus on the effect HEVC (High Efficiency Video Coding) compression artifacts have on the visual quality of a broadcasted and streamed video, as this is a requirement of immense importance for these industries. We chose the Video Multi-Method Assessment Fusion (VMAF) metric as our base measure to map visual quality of HEVC compression artifacts to five visual quality levels. The original VMAF classification was changed to reflect High Definition (HD) resolution images. We trained a DenseNet network to classify compressed images into five visual categories using the dataset generated by the modified VMAF. DenseNet was chosen for its ability to process HD images. Our evaluations have shown that our no-reference metric achieves an impressive average accuracy of 94.13% in classifying the visual quality of compressed images.

Distribution patterns and drivers of urban green space and plant diversity in Haikou, China.

Investigating historical and ongoing changes in urban green space (UGS) and urban plant diversity (UPD) provides critical insights into urban ecology and urban planning development. The present study illuminates some of the transformations which can occur in rapidly developing urban landscapes. In this work, we used 30 m resolution images from the Landsat 5 satellite from 2015 to investigate UGS patterns in Haikou City, China. Metrics of UPD were obtained using field surveys, allowing the proportion of UGS and UPD to be determined in each urban functional unit (UFU) of Haikou. The results revealed that leisure and entertainment areas (such as parks) had the highest diversity, whereas roads and transportation hubs had the lowest. More frequent anthropogenic maintenance had a positive effect on the total number of species, including cultivated, tree, and herb species. Similarly, increased watering frequency had a positive impact on the diversity of cultivated and shrub species. By providing demonstrating a crucial link between UGS and UPD, the results provide valuable information for planning sustainable urban development in Haikou City and other tropical regions. They highlight the important role of UGS in maintaining biodiversity and providing a range of ecosystem services. This research will inform policymakers and urban planners about the need to consider UGS and UPD in urban planning and management process, in order to promote sustainability and conservation of biodiversity.

Multi-modality multi-scale cardiovascular disease subtypes classification using Raman image and medical history

Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved outstanding performance than conventional machine learning methods. However, these existing deep learning methods still confront some challenges in classifying subtypes of CVD. For example, the nuance between subtypes is quite hard to capture and represent by intelligent models due to the chillingly similar shape of RS sequences. Moreover, medical history information is an essential resource for distinguishing subtypes, but they are underutilized. In light of this, we propose a multi-modality multi-scale model called M3S, which is a novel deep learning method with two core modules to address these issues. First, we convert RS data to various resolution images by the Gramian angular field (GAF) to enlarge nuance, and a two-branch structure is leveraged to get embeddings for distinction in the multi-scale feature extraction module. Second, a probability matrix and a weight matrix are used to enhance the classification capacity by combining the RS and medical history data in the multi-modality data fusion module. We perform extensive evaluations of M3S and found its outstanding performance on our in-house dataset, with accuracy, precision, recall, specificity, and F1 score of 0.9330, 0.9379, 0.9291, 0.9752, and 0.9334, respectively. These results demonstrate that the M3S has high performance and robustness compared with popular methods in diagnosing CVD subtypes.