Resolution Ones Research Articles

The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models

AbstractFor a number of years research has been carried out in several centres which has demonstrated the potential benefits of 100‐m scale models for a range of meteorological phenomena. More recently, some meteorological services have started to consider seriously the operational implementation of practical hectometric models. Many, but by no means all, of the applications are likely to relate to urban areas, where the enhanced resolution has obvious benefits. This article is concerned with the issues that need to be addressed to bridge the gap between research at 100‐m scales and practical models. We highlight a number of key issues that need to be addressed, with suggestions of important avenues for future development. An overarching issue is the high computational cost of these models. Although some ideas to reduce this are presented, it will always be a serious constraint. This means that the benefits of these models over lower resolution ones, or other techniques for generating high‐resolution forecasts, will need to be clearly understood, as will the trade‐offs with resolution. We discuss issues with model dynamical cores and physics–dynamics coupling. There are a number of challenges around model parameterisations, where some of the traditional problems (e.g., convection) become easier but a number of new challenges (e.g., around surface parameterisations) appear. Observational data at these scales present a challenge and novel types of observations will need to be considered. Data assimilation will be needed for short‐range forecasts, but there is currently little knowledge of this, although some of the likely issues are clear. An ensemble approach will be essential in many cases (e.g., convection), but research is needed into ensembles at these scales and significant work on post‐processing systems is required to make the best use of models at these grid lengths.

Combining optical flow and Swin Transformer for Space-Time video super-resolution

Space–time video super-resolution is a task that aims to interpolate low frame rate, low resolution videos to high frame rate, high resolution ones. While existing Transformer-based methods have achieved results comparable to convolutional neural networks-based methods, the computational cost of Transformer limits its performance with constrained computational resources. Moreover, Swin Transformer may fail to fully exploit the spatio-temporal information of video frames due to the limitation of window size, impeding its effectiveness in handling large motions. To address these limitations, we propose an end-to-end space–time video super-resolution architecture based on optical flow alignment and Swin Transformer. The alignment module is introduced to extract spatio-temporal information from adjacent frames without significantly increasing the computational burden. Additionally, we design a residual convolution layer to enhance the translational invariance of the features extracted by Swin Transformer and introduces additional nonlinear transformations. Experimental results demonstrate that our proposed method achieves superior performance on various benchmark datasets compared to state-of-the-art methods. In terms of Peak Signal-to-Noise Ratio, our method outperforms the state-of-the-art methods by at least 0.15 dB on Vimeo-Medium dataset.

Estimation and Evaluation of 15 Minute, 40 Meter Surface Upward Longwave Radiation Downscaled from the Geostationary FY-4B AGRI

Surface upward longwave radiation (SULR) is one of the four components of surface net radiation. Geostationary satellites can provide high temporal but coarse spatial resolution SULR products. Downscaling coarse SULR to a higher resolution is important for fine-scale thermal condition monitoring. Statistical regression downscaling is widely used due to its simplicity and is built on the assumption that the thermal parameter like land surface temperature (LST) or SULR has a relationship with the related surface factors like the normalized difference vegetation index (NDVI), and the relationship remains unchanged in any scales. In this study, to establish the relationship between SULR and the related surface factors, we chose the multiple linear regression (MLR) model and five surface factors (i.e., the modified normalized difference water index (MNDWI), normalized difference built-up and soil index (NDBSI), NDVI, normalized moisture difference index (NMDI), and urban index (UI)) to drive the downscaling process. Additionally, a step-by-step downscaling strategy was applied to reach the 100-fold increase in spatial resolution, transitioning the estimated SULR from 4 km of the advanced geostationary radiation imager (AGRI) onboard FengYun-4B (FY-4B) satellite to 40 m of the visual and infrared multispectral imager (VIMI) in infrared spectrum onboard GaoFen5-02 (GF5-02). Finally, we evaluated the downscaling results by comparing the downscaled SULR values with the in situ measured SULR and GF5-02-calculated SULR, and the root mean square errors (RMSEs) were 19.70 W/m2 and 24.86 W/m2, respectively. Throughout this MLR-based step-by-step downscaling method (high-frequency data from FY-4B and high spatial resolution data from GF5-02), high spatiotemporal SULR (15 min temporal resolution, 40 m spatial resolution) were successfully generated instead of coarse spatial resolution ones from the FY-4B satellite or a coarse temporal resolution one from the GF5-02 satellite, relieving the above-mentioned conflict to some extent.

Use of Generative Adversarial Network Algorithm in Super-Resolution Images to Increase the Quality of Digital Elevation Models Based on ALOS PALSAR Data

Digital elevation models are responsible for providing altimetric information on a surface to be mapped. While global models of low and medium spatial resolution are available open source by several space agencies, the high- resolution ones, which are utilized in scales 1:25,000 and larger, are scarce and expensive. Here we address this limitation by the utilization of deep learning algorithms coupled with Single Image Super-Resolution techniques in digital elevation models to obtain better spatial quality versions from lower resolution inputs. The development of a GAN-based (Generative Adversarial Network-based) methodology enables the improvement of the initial spatial resolution of low-resolution images. In the geospatial data context, for example, these algorithms can be used with digital elevation models and satellite images. The methodological approach uses a dataset with digital elevation models SRTM (Shuttle Radar Topography Mission) (30 meters of spatial resolution) and ALOS PALSAR (12.5 meters of spatial resolution), created with the objective of allowing the study to be carried out, promoting the emergence of new research groups in the area as well as enabling the comparison between the results obtained. It has been found that by increasing the number of iterations the performance of the generated model was improved and the quality of the generated image increased. Furthermore, the visual analysis of the generated image against the high- and low-resolution ones showed a great similarity between the first two.

A high resolution compact all-fiber spectrometer based on periodic refractive index modulation

The realization of a miniaturized spectrometer with high resolution is highly desired but is still a big challenge. Although all-fiber spectrometers based on speckle detection show their great potential for high resolution ones, their long fiber lengths set the greatest obstacle for the miniaturized design. Here, we demonstrate a compact all-fiber speckle spectrometer by using cascading coreless fibers and photonic crystal fibers. A unique cascaded structure readily excites more guided modes, in which the speckle patterns are formed by modal interferences. Using only a 10 cm-long fiber with 20-segment spliced elements, a resolution of 0.03 nm over a bandwidth from 1540 to 1560 nm is achieved. The spectral resolution is comparable to that of a 2 m multimode fiber spectrometer and approximately 20 times higher than that of the same length multimode fiber. Narrow linewidth and broadband spectra are individually reconstructed to demonstrate the excellent performance of the spectrometer. The proposed processing technique of the dispersive element is versatile, reproducible, and controllable, promising for different application scenarios.

An Enhanced Fluid Registration for Image Multi-Frame Super Resolution

The main idea of multi-frame super resolution (SR) algorithms is to recover a single high-resolution image from a sequence of low resolution ones of the same object. The success of the SR approaches is often related to a well registration and restoration steps. Therefore, we propose a new approach based on fluid image registration and we use a second order partial differential equation (PDE) to treat both the registration and restoration steps that guarantees the success of SR algorithms. Since the registration step is usually a variational ill-posed model, a mathematical study is needed to check the existence of the solution to the regularized problem. Thus, we prove the existence and uniqueness of the well posed fluid image registration and assure also the existence of the used second order PDE in the restoration step. The results show that the proposed method is competitive with the existing methods.

An improved PDE-constrained optimization fluid registration for image multi-frame super resolution

The main idea of multi-frame super resolution (SR) algorithms is to recover a single high-resolution image from a sequence of low resolution ones of the same object. The success of the SR approaches is often related to a well registration and restoration steps. Therefore, we propose a new approach based on a partial differential equation (PDE)-constrained optimization fluid image registration and we use a fourth order PDE to treat both the registration and restoration steps that guarantee the success of SR algorithms. Since the registration step is usually a variational ill-posed model, a mathematical study is needed to check the existence of the solution to the regularized problem. Thus, we prove the existence and of the well posed fluid image registration and assure also the existence of the used second order PDE in the restoration step. The results show that the proposed method is competitive with the existing methods.

Video super-resolution based on deep learning: a comprehensive survey

Video super-resolution (VSR) is reconstructing high-resolution videos from low resolution ones. Recently, the VSR methods based on deep neural networks have made great progress. However, there is rarely systematical review on these methods. In this survey, we comprehensively investigate 37 state-of-the-art VSR methods based on deep learning. It is well known that the leverage of information contained in video frames is important for video super-resolution. Thus we propose a taxonomy and classify the methods into seven sub-categories according to the ways of utilizing inter-frame information. Moreover, descriptions on the architecture design and implementation details are also included. Finally, we summarize and compare the performance of the representative VSR methods on some benchmark datasets. We also discuss the applications, and some challenges, which need to be further addressed by researchers in the community of VSR. To the best of our knowledge, this work is the first systematic review on VSR tasks, and it is expected to make a contribution to the development of recent studies in this area and potentially deepen our understanding of the VSR techniques based on deep learning.

Low-Resolution Infrared Array Sensor for Counting and Localizing People Indoors: When Low End Technology Meets Cutting Edge Deep Learning Techniques

In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 × 12 and 8 × 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 × 12. For frames of size 8 × 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection.

Physical processes of summer extreme rainfall interannual variability in Eastern China—part II: evaluation of CMIP6 models

Eastern China is regularly exposed to extreme precipitation with significant socio-economical consequences. Following an observational analysis in a first part of this study, here the ability of the Coupled Model Intercomparison Project Phase 6 models to reproduce the main modes of interannual variability of 5-day summer extreme precipitation over eastern China is evaluated, using an empirical orthogonal teleconnection (EOT) method. These models capture the main patterns and magnitudes of the different EOT patterns, although the North China mode is less well represented. Models also reproduce the dynamical features associated with each mode. There is no systematic improvement in the ability of models to simulate either the pattern or the 5-day intensity when using higher resolution models compared to coarser resolution ones. Instead, multi-member or multi-model ensembles lead to results closer to observations. Using a low mitigation projection pathway (SSP-370), it is shown that the risk of the most extreme 5-day precipitation events by about 40%, 80% and more than 150% for global-mean warming levels, relative to 1850–1900, of + 1.5, + 2 and + 3 ^{circ }C respectively. This increase is found to be more significant for 5-days events than for seasonal scale precipitation, consistent with previous studies.

RESOLUTION ENHANCEMENT OF INFRARED THERMAL IMAGING BY PANSHARPENING ALGORITHMS

Abstract. One common tool in Cultural Heritage inspections is thermal cameras, which are sensitive to the infrared part of the electromagnetic spectrum. But the resolution of these sensors is quite lower than other kinds like visible spectrum range cameras. Typically, the sensors in thermal cameras do not exceed the megapixel frontier. This limitation becomes a problem when trying to combine the information from the thermal images with data from other sensors with much higher resolution such as visible RGB cameras in the same project.In Remote Sensing, algorithms have been designed to fuse multispectral images with panchromatic images (in origin from satellite platforms) to enhance the resolution of lower resolution images with higher resolution ones. These processes are known as pansharpening. Although pansharpening procedures are widely known, they have not been tested working with thermal imaging. The first approach of merging thermal and visual spectrum images to enhance the resolution of the original thermal image involved applying the intensity-hue-saturation (IHS) algorithm (Lagüela et al., 2012, Kuenzer and Dech, 2013). These works only studied one particular algorithm and they did not include any quality study of the results.Our work contains a complete review of a bigger pansharpening algorithms’ set and provides an in-depth study of thermal imaging pansharpening, with a numerical assessment. Our research allows the use of thermal sensors with a lower resolution than other types of sensors used simultaneously in the same project.

Effects of Spatial Resolution on the Satellite Observation of Floating Macroalgae Blooms

Satellite images with different spatial resolutions are widely used in the observations of floating macroalgae booms in sea surface. In this study, semi-synchronous satellite images with different resolutions (10 m, 16 m, 30 m, 50 m, 100 m, 250 m and 500 m) acquired over the Yellow Sea, are used to quantitatively assess the effects of spatial resolution on the observation of floating macroalgae blooms of Ulva prolifera. Results indicate that the covering area of macroalgae-mixing pixels (MM-CA) detected from high resolution images is smaller than that from low resolution images; however, the area affected by macroalgae blooms (AA) is larger in high resolution images than in low resolution ones. The omission rates in the MM-CA and the AA increase with the decrease of spatial resolution. These results indicate that satellite remote sensing on the basis of low resolution images (especially, 100 m, 250 m, 500 m), would overestimate the covering area of macroalgae while omit the small patches in the affected zones. To reduce the impacts of overestimation and omission, high resolution satellite images are used to show the seasonal changes of macroalgae blooms in 2018 and 2019 in the Yellow Sea.

A deep convolutional encoder-decoder architecture for autonomous fault detection of PV plants using multi-copters

This study presents an autonomous fault detection method for a wide range of common failures and defects which are visually visible on PV modules. In this paper, we focus especially on detection of bird’s drops as a very typical defect on the PV modules. As a crucial prerequisite, a data-set of aerial imageries of the PV strings affected by bird’s drops were collected through several experimental flight by multi-copters in order to train an accurate fully convolutional deep network. These images are divided into three groups, namely, training, testing, and validation parts. For the purpose of bird’s drops segmentation, an improved encoder-decoder architecture is employed. In this regard, a modified VGG16 model is used as a backbone for the encoder part. The encoder of the network has a very flexible architecture that can be modified and trained for any other visual failure detection. Later on, extracted feature maps of images are imported into a decoder network to map the low resolution features to full resolution ones for pixel-wise segmentation. In addition, an image object positioning algorithm is presented to find the exact position of detected failures in local coordinate system. In a post-processing step, the detected damages are prioritized based on various parameters such as severity of shading and extent of impact on the PV module’s output current. For further validation, different affected PV modules were characterized according to the output patterns of the classification step in order to accurately evaluate the effect of birds’ drops and consequent shading on the parameters of PV modules based on their severity and location. Finally, the training and testing results demonstrate that the proposed FCN network is able to predict precisely covered pixels by bird’s drops on PV modules at pixel level with average accuracies of 98% and 93% for training and testing, respectively.

Skill in Stone Knapping: an Ecological Approach

Skill has allowed lithic analyses to expand their scope beyond the limits set by a representational understanding of practices and sociocultural dynamics. It remains excluded from coarse archaeological contexts in favour of higher resolution ones however. Such coarse contexts are ubiquitous and must be included to broaden description, interpretation and theorization into broader and more heterogeneous narrative landscapes. This paper argues that skill is key to including lithic practices from coarser archaeological palimpsests, provided it is reframed as a process immanent to any cultural practice that conjoins with other processes to shape contexts of various scales. Second, skill must be anchored with a set of core concepts—technical difficulty, accidents and execution quality—that each knapping event and every lithic assemblage actualizes, regardless of scale. Third, methodologies must be built using this set of core concepts and adapted to a site’s specifics. Using such a methodology, I describe learning patterns, skilled reduction sequences and spatial patterning in the plowed fields of La Martre (Quebec, Canada), where millennia of continuous occupation and hundreds of thousands of lithic remains have been mixed up in a dense and homogeneous layer. I show that understanding skill as a trans-scalar process can help free lithic analyses from prior, bounded and familiar units of analysis. It can and should be used first to draw broader patterns that connect contextually specific lithic expressions. It affords for scalable analyses that can help expand the scope of the depositional contexts archaeologists routinely work with.

WITHDRAWN: Super resolution using generative adversarial networks

Abstract Resolution has continually been a problem since the term ‘picture’ or ‘image’ came into existence. There have been many researches on improvising the resolution of an image however none could create very fast sufficient strategies to get the correct effects that can manage both time and production of best outputs. So, in this, we are going to discover about super resolution which has been a hassle for many years with the usage of signal processing strategies. But now we are able to clear up this trouble using deep learning strategies. The method we endorse here can flip a low-resolution photo/video to high resolution ones, up to a pleasant and exceptional level. We aim to obtain these using GANs (Generative Adversarial Networks) at considerable upscaling factors.

Willmott Fuzzy Implication in Fuzzy Databases

The object of the research are fuzzy functional dependencies on given relation scheme, and the question of their obtaining using the classical and innovated techniques. The attributes of the universal set are associated to the elements of the unit interval, and are turned into fuzzy formulas in this way. We prove that the dependency (which is treated as a fuzzy formula with respect to appropriately chosen valuation) is valid whenever it agrees with the attached two-elements fuzzy relation instance. The opposite direction of the claim is proven to be incorrect in this setting. Generalizing things to sets of attributes, we prove that particular fuzzy functional dependency follows form a set of fuzzy dependencies (in both, the world of two-element and the world of arbitrary fuzzy relation instances) if and only if the dependency is valid with respect to valuation anytime the set of fuzzy formulas agrees with the valuation. The results derived in paper show that the classical techniques in the procedure for generating new fuzzy dependencies may be replaced by the resolution ones, and hence automated. The research is conducted with respect to Willmott fuzzy implication operator

A multi-frame super-resolution based on new variational data fidelity term

The main idea of multi-frame super-resolution (SR) algorithm is to recover a single high-resolution (HR) image from a sequence of low resolution ones of the same scene. Since the restoration step of super-resolution algorithms is always an ill-posed problem, the choice of the fidelity term and the regularization are always crucial. In this paper, we propose a new variational SR framework based on an automatic selection of the weighting parameter that control the balance between the L1 and L2 fidelity terms, which handle different type of noise distributions. Concerning the regularization, we use the combined total variation (TV) and the total variation of the first derivatives (TV2) model with a new implementation of the Primal-dual algorithm to solve the corresponding discretized problem. The obtained results are compared with some competitive algorithms and confirm that the proposed method has much benefices over the others in avoiding some undesirable artifacts.

3D Face Model Super-Resolution Based on Radial Curve Estimation

Consumer depth cameras bring about cheap and fast acquisition of 3D models. However, the precision and resolution of these consumer depth cameras cannot satisfy the requirements of some 3D face applications. In this paper, we present a super-resolution method for reconstructing a high resolution 3D face model from a low resolution 3D face model acquired from a consumer depth camera. We used a group of radial curves to represent a 3D face. For a given low resolution 3D face model, we first extracted radial curves on it, and then estimated their corresponding high resolution ones by radial curve matching, for which Dynamic Time Warping (DTW) was used. Finally, a reference high resolution 3D face model was deformed to generate a high resolution face model by using the radial curves as the constraining feature. We evaluated our method both qualitatively and quantitatively, and the experimental results validated our method.

Fat navigators and Moiré phase tracking comparison for motion estimation and retrospective correction.

To compare motion tracking by two modern methods (fat navigators [FatNavs] and Moiré phase tracking [MPT]) as well as their performance for retrospective correction of very high resolution acquisitions. A direct comparison of FatNavs and MPT motion parameters was performed for several deliberate motion patterns to estimate the agreement between methods. In addition, two different navigator resolution were applied. 0.5 mm isotropic MP2RAGE images with simultaneous MPT and FatNavs tracking were acquired in 9 cooperative subjects with no intentional motion. Retrospective motion corrections based on both tracking modalities were compared qualitatively and quantitatively. The FatNavs impact on quantitative T1 maps was also investigated. Both methods showed good agreement within a 0.3 mm/° margin in subjects that moved very little. Higher resolution FatNavs (2 mm) showed overall better agreement with MPT than 4 mm resolution ones, except for fast and large motion. The retrospective motion corrections based on MPT or FatNavs were at par in 33 cases out of 36, and visibly improved image quality compared to the uncorrected images. In separate fringe cases, both methods suffered from their respective potential shortcomings: unreliable marker attachment for MPT and poor temporal resolution for FatNavs. The magnetization transfer induced by the navigator RF pulses had a visible impact on the T1 values distribution, with a shift of the gray and white matter peaks of 12 ms at most. This work confirms both FatNavs and MPT as excellent retrospective motion correction methods for very high resolution imaging of cooperative subjects.

Joint bilateral propagation upsampling for unstructured multi-view stereo

In this paper, we explore a new way to accelerate and densify unstructured multi-view stereo (MVS). While many unstructured MVS algorithms have been proposed, we discover that the image-guided resizing can easily and significantly benefit their 3D reconstruction results in both efficiency and completeness. Therefore, we build our framework upon a novel selective joint bilateral upsampling and depth propagation strategy. First, we downsample the input unstructured images into lower resolution ones and perform the MVS calculation to efficiently obtain depth and normal maps from these resized pictures. Then, the proposed algorithm upsamples the normal maps with the guidance of input images, and jointly take them into consideration to recover the low-resolution depth maps into high resolution with geometry details simultaneously enriched. Finally by adaptively fusing the reconstructed depth and normal maps, we construct the final dense 3D scene. Quantitative results validate the efficiency and effectiveness of the proposed method.