Finite element analysis of multi-piece post-crown restoration using different types of adhesives

The multi-piece post-crown technique is more effective in restoring residual root than other restoration techniques.Various types of adhesives have different material properties that affect restoration.Therefore,the choice of adhesive is particularly important for patients.However,the effect of different kinds of adhesives was not too precise by experimental methods when concerning about individual differences of teeth.One tooth root can only be restored with one type of adhesive in experiment.After the mechanical test,this tooth root cannot be restored with other adhesives.With the help of medical imaging technology,reverse engineering and finite element analysis,a molar model can be reconstructed precisely and restored using different types of adhesives.The same occlusal and chewing loads were exerted on the same restored residual root models with different types of adhesives separately.Results of von Mises stress analysis showed that the adhesives with low Young’s modulus can protect the root canal effectively.However,a root canal concentration is apparently produced around the root canal orifice when chewing.Adhesives with large Young’s modulus can buffer the stress concentration of the root canal orifice.However,the root canal tissue may be destroyed because the adhesive is too hard to buffer the load.

Restoration or Rehabilitation of the Faleme River Affected by Mining Activities: What Methods?

The Faleme River, a West Africa long transboundary stream (625 km) and abundant flow (>1100 million m3) is affected by severe erosion because of mining activities that takes place throughout the riverbed. To preserve this important watercourse and ensure the sustainability of its services, selecting and implementing appropriates restorations techniques is vital. In this context, the purpose of this paper was to present an overview of the actions and techniques that can be implemented for the restoration/rehabilitation of the Faleme. The methodological approach includes field investigation, water sampling, literature review with cases studies and SWOT analysis of the four methods presented: river dredging, constructed wetlands, floating treatment wetlands and chemical precipitation (coagulation and flocculation). The study confirmed the pollution of the river by suspended solids (TSS > 1100 mg/L) and heavy metals such as iron, zinc, aluminium, and arsenic. For the restoration methods, it was illustrated through description of their mode of operation and through some case studies presented, that all the four methods have proven their effectiveness in treating rivers but have differences in their costs, their sustainability (detrimental to living organisms or causing a second pollution) and social acceptance. They also have weaknesses and issues that must be addressed to ensure success of rehabilitation. For the case of the Faleme river, after analysis, floating treatment wetlands are highly recommended for their low cost, good removal efficiency if the vulnerability of the raft and buoyancy to strong waves and flow is under control.

Phytoremediation Strategies for Heavy Metal Contamination: A Review on Sustainable Approach for Environmental Restoration

Current globalization trends and important breakthroughs globally need a complete study of heavy metal contamination, its causes, its impacts on human and environmental health, and different remediation strategies. Heavy metal pollution is mostly produced by urbanization and industry, which threatens ecosystems and human health. Herein, we discuss a sustainable environmental restoration strategy employing phytoremediation for heavy metal pollution, the carcinogenic, mutagenic, and cytotoxic effects of heavy metals such as cadmium, copper, mercury, selenium, zinc, arsenic, chromium, lead, nickel, and silver, which may be fatal. Phytoremediation, which was prioritized, uses plants to remove, accumulate, and depollute pollutants. This eco-friendly method may safely collect, accumulate, and detoxify toxins using plants, making it popular. This study covers phytostabilization, phytodegradation, rhizodegradation, phytoextraction, phytovolatilization, and rhizofiltration. A phytoremediation process’s efficiency in varied environmental circumstances depends on these components’ complex interplay. This paper also introduces developing phytoremediation approaches including microbe-assisted, chemical-assisted, and organic or bio-char use. These advancements attempt to overcome conventional phytoremediation’s limitations, such as limited suitable plant species, location problems, and sluggish remediation. Current research includes machine learning techniques and computer modeling, biostimulation, genetic engineering, bioaugmentation, and hybrid remediation. These front-line solutions show that phytoremediation research is developing towards transdisciplinary efficiency enhancement. We acknowledge phytoremediation’s promise but also its drawbacks, such as site-specific variables, biomass buildup, and sluggish remediation, as well as ongoing research to address them. In conclusion, heavy metal pollution threatens the ecology and public health and must be reduced. Phytoremediation treats heavy metal pollution in different ways. Over time, phytoremediation systems have developed unique ways that improve efficiency. Despite difficulties like site-specificity, sluggish remediation, and biomass buildup potential, phytoremediation is still a vital tool for environmental sustainability.

Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block-GAN

In the context of high compression rates applied to Joint Photographic Experts Group(JPEG)images through lossy compression techniques,image-blocking artifacts may manifest.This necessitates the restoration of the image to its original quality.The challenge lies in regenerating significantly compressed images into a state in which these become identifiable.Therefore,this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks(GAN).The generator in this network is based on theU-Net architecture.It features a newhourglass structure that preserves the characteristics of the deep layers.In addition,the network incorporates two loss functions to generate natural and high-quality images:Low Frequency(LF)loss and High Frequency(HF)loss.HF loss uses a pretrained VGG-16 network and is configured using a specific layer that best represents features.This can enhance the performance in the high-frequency region.In contrast,LF loss is used to handle the low-frequency region.The two loss functions facilitate the generation of images by the generator,which can mislead the discriminator while accurately generating high-and low-frequency regions.Consequently,by removing the blocking effects frommaximum lossy compressed images,images inwhich identities could be recognized are generated.This study represents a significant improvement over previous research in terms of the image resolution performance.

Characterisation of the Bacteria and Archaea Community Associated with Wild Oysters, At Three Possible Restoration Sites in the North Sea

With 85% of the global oyster reefs destroyed, there is an urgent need for large scale restoration to benefit from the ecosystem services provided by biogenic oyster reefs and their associated biodiversity, including microorganisms that drive marine biogeochemical cycles. This experiment established a baseline for the monitoring of the bacterial and archaeal community associated with wild oysters, using samples from their immediate environment of the Voordelta, with cohabiting Crassostrea gigas and Ostrea edulis, Duikplaats with only C. gigas attached to rocks, and the Dansk Skaldyrcentre, with no onsite oysters. The microbial profiling was carried out through DNA analysis of samples collected from the surfaces of oyster shells and their substrate, the sediment and seawater. Following 16S rRNA amplicon sequencing and bioinformatics, alpha indices implied high species abundance and diversity in sediment but low abundance in seawater. As expected, Proteobacteria, Bacteroidetes, Firmicutes and Thaumarchaeota dominated the top 20 OTUs. In the Voordelta, OTUs related to Colwellia, Shewanella and Psychrobium differentiated the oysters collected from a reef with those attached to rocks. Duikplaats were distinct for sulfur-oxidizers Sulfurimonas and sulfate-reducers from the Sva 0081 sediment group. Archaea were found mainly in sediments and the oyster associated microbiome, with greater abundance at the reef site, consisting mostly of Thaumarchaeota from the family Nitrosopumilaceae. The oyster free site displayed archaea in sediments only, and algal bloom indicator microorganisms from the Rhodobacteraceae, Flavobacteriaceae family and genus [Polaribacter] huanghezhanensis, in addition to the ascidian symbiotic partner, Synechococcus. This study suggests site specific microbiome shifts, influenced by the presence of oysters and the type of substrate.

Ecological problems and ecological restoration zoning of the Aral Sea

The Aral Sea was the fourth largest lake in the world but it has shrunk dramatically as a result of irrational human activities, triggering the "Aral Sea ecological crisis". The ecological problems of the Aral Sea have attracted widespread attention, and the alleviation of the Aral Sea ecological crisis has reached a consensus among the five Central Asian countries(Kazakhstan, Uzbekistan, Tajikistan, Kyrgyzstan, and Turkmenistan). In the past decades, many ecological management measures have been implemented for the ecological restoration of the Aral Sea. However, due to the lack of regional planning and zoning, the results are not ideal. In this study, we mapped the ecological zoning of the Aral Sea from the perspective of ecological restoration based on soil type, soil salinity, surface water, groundwater table, Normalized Difference Vegetation Index(NDVI), land cover, and aerosol optical depth(AOD) data. Soil salinization and salt dust are the most prominent ecological problems in the Aral Sea. We divided the Aral Sea into 7 first-level ecological restoration subregions(North Aral Sea catchment area in the downstream of the Syr Darya River(Subregion Ⅰ);artificial flood overflow area in the downstream of the Aral Sea(Subregion Ⅱ);physical/chemical remediation area of the salt dust source area in the eastern part of the South Aral Sea(Subregion Ⅲ);physical/chemical remediation area of severe salinization in the central part of the South Aral Sea(Subregion Ⅳ);existing water surface and potential restoration area of the South Aral Sea(Subregion Ⅴ);Aral Sea vegetation natural recovery area(Subregion Ⅵ);and vegetation planting area with slight salinization in the South Aral Sea(Subregion Ⅶ)) and 14 second-level ecological restoration subregions according to the ecological zoning principles. Implementable measures are proposed for each ecological restoration subregion. For Subregion Ⅰ and Subregion Ⅱ with lower elevations, artificial flooding should be carried out to restore the surface of the Aral Sea. Subregion Ⅲ and Subregion Ⅳ have severe salinization, making it difficult for vegetation to grow. In these subregions, it is recommended to cover and pave the areas with green biomatrix coverings and environmentally sustainable bonding materials. In Subregion Ⅴ located in the central and western parts of the South Aral Sea, surface water recharge should be increased to ensure that this subregion can maintain normal water levels. In Subregion Ⅵ and Subregion Ⅶ where natural conditions are suitable for vegetation growth, measures such as afforestation and buffer zones should be implemented to protect vegetation. This study could provide a reference basis for future comprehensive ecological management and restoration of the Aral Sea.

Resilience-Oriented Load Restoration Method and Repair Strategies for Regional Integrated Electricity-Natural Gas System

The rising frequency of extreme disaster events seriously threatens the safe and secure operation of the regional integrated electricity-natural gas system(RIENGS).With the growing level of coupling between electric and natural gas systems,it is critical to enhance the load restoration capability of both systems.This paper proposes a coordinated optimization strategy for resilience-enhanced RIENGS load restoration and repair scheduling and transforms it into a mixed integer second-order cone programming(MISOCP)model.The proposed model considers the distribution network reconfiguration and the coordinated repair strategy between the two systems,minimizing the total system load loss cost and repair time.In addition,a bi-directional gas flow model is used to describe the natural gas system,which can provide the RIENGS with more flexibility for load restoration during natural gas system failure.Finally,the effectiveness of the proposed approach is verified by conducting case studies on the test systems RIENGS E13-G7 and RIENGS E123-G20.

From single to combinatorial therapies in spinal cord injuries for structural and functional restoration

Spinal cord injury results in paralysis, sensory disturbances, sphincter dysfunction, and multiple systemic secondary conditions, most arising from autonomic dysregulation. All this produces profound negative psychosocial implications for affected people, their families, and their communities;the financial costs can be challenging for their families and health institutions. Treatments aimed at restoring the spinal cord after spinal cord injury, which have been tested in animal models or clinical trials, generally seek to counteract one or more of the secondary mechanisms of injury to limit the extent of the initial damage. Most published works on structural/functional restoration in acute and chronic spinal cord injury stages use a single type of treatment: a drug or trophic factor, transplant of a cell type, and implantation of a biomaterial. Despite the significant benefits reported in animal models, when translating these successful therapeutic strategies to humans, the result in clinical trials has been considered of little relevance because the improvement, when present, is usually insufficient. Until now, most studies designed to promote neuroprotection or regeneration at different stages after spinal cord injury have used single treatments. Considering the occurrence of various secondary mechanisms of injury in the acute and sub-acute phases of spinal cord injury, it is reasonable to speculate that more than one therapeutic agent could be required to promote structural and functional restoration of the damaged spinal cord. Treatments that combine several therapeutic agents, targeting different mechanisms of injury, which, when used as a single therapy, have shown some benefits, allow us to assume that they will have synergistic beneficial effects. Thus, this narrative review article aims to summarize current trends in the use of strategies that combine therapeutic agents administered simultaneously or sequentially, seeking structural and functional restoration of the injured spinal cord.

Impact of Vegetation Restoration on Soil Fungal Community Structure in Karst Rocky Desertification Areas

In this paper,managed forest(MF)and natural forest(NF)in the Huajiang Demonstration Zone of Guanling,Guizhou were selected as research objects,and cropland(CL)was taken as control.High-throughput sequencing technology was used to study the characteristics of fungal community composition and species diversity in the surface(0-10 cm)soil of each restoration measure,in order to reveal the dominant soil fungal groups and fungal community composition in karst rocky desertification areas,which was conducive to a more comprehensive understanding of the soil conditions of different vegetation restoration measures.Research has shown that vegetation restoration significantly affected the diversity of soil fungal community,with significant increases in Sob index,Ace index,and Chao index.The vegetation restoration has significantly changed the composition of fungal community.The dominant fungi in the CL topsoil are Sordariomycetes(62.28%),Dothideomycetes(12.34%),and Eurotiomycetes(9.12%);the dominant fungi in the MF soil are Sordariomycetes(45.05%),Dothideomycetes(14.74%),and Mortierellomycetes(10.40%);the dominant fungi in the NF soil are unclassified fungal community(26.38%),Sordariomycetes(19.78%),and Agaricomycetes(13.82%).Vegetation restoration has changed the key fungal groups in the soil.Sordariomycetes,Fusarium,and Setophoma are the key dominant fungal groups in CL soil;Dioszegia is key dominant fungal group in MF soil;c_unclassified_k_Fungi,p_unclassified_k_Fungi,o_unclassified_k_Fungi,f_unclassified_k_Fungi,g_unclassified_k_Fungi,Teichospora,and Diaporthe are key dominant fungal groups in NF soil.

Progress and Prospects of the Natural Restoration of Damaged Vegetation after the Earthquake

Vegetation plays an important role in soil and water conservation, water conservation and carbon sequestration of an ecosystem. The restoration of damaged vegetation is of great significance to the maintenance of species diversity and the restoration of the regional ecological environment. It is also one of the most effective measures to improve the fragile ecosystem. This paper summarizes the research results from decades of damaged vegetation recovery in the process of vegetation recovery, the main driving factor and the restoration mode.

Model-based deep learning for fiber bundle infrared image restoration

As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is proposed.Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep learning.By building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated simultaneously.Numerical results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise.On the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements.The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.

Interactive Restoration of Three-Dimensional Implicit Surface with Irregular Parts

Implicit surface generation based on the interpolation of surface points is one of the well-known modeling methods in the area of computer graphics.Several methods for the implicit surface reconstruction from surface points have been proposed on the basis of radial basis functions,a weighted sum of local functions,splines,wavelets,and combinations of them.However,if the surface points contain errors or are sparsely distributed,irregular components,such as curvature-shaped redundant bulges and unexpectedly generated high-frequency components,are commonly seen.This paper presents a framework for restoring irregular components generated on and around surfaces.Users are assumed to specify local masks that cover irregular components and parameters that determine the degree of restoration.The algorithm in this paper removes the defects based on the user-specific masks and parameters.Experiments have shown that the proposed methods can effectively remove redundant protrusions and jaggy noise.

Effects of lag time in forest restoration and management

The increased speed of global change and associated high severity disturbances,in conjunction with the increasing suite of societal expectations on forests,suggest that the timeliness of interventions to encourage the adaptive capacity of ecosystems and to reduce negative impacts in regards to provision of ecosystem services is increasingly relevant.To address this issue,we expand the concept of lag time as used in ecological discussions into a forest management context.In this context,lag times have earlier starting and later ending points and can be separated into different components.These components include the delay till detection,decision making,and implementation,followed by ecological lag time and the time till ecosystem services are provided at acceptable levels.The first three components are influenced by the availability of information,the lack of which can extend lag times.Also,the lengths of components are not simply additive but they interact.For example,treatment preparation due to a quicker detection can lead to shorter decision and implementation lag times.We highlight the benefits of addressing the various components of lag time in forestry operations.Especially when considering adaptive capacity in times of global change,our analysis suggests that all aspects of the forestry sector are challenged to consider how to optimize lag times.Last,we propose that such issues need to be considered with any management action and are especially relevant in discussions whether the best strategy after disturbances or in the light of global change is to adopt a passive approach and let natural ecosystem processes play out on their own or whether active management is better suited to ensure a more rapid and fitting ecosystem response to facilitate the continued provision of ecosystem services.

Reliable Failure Restoration with Bayesian Congestion Aware for Software Defined Networks

SoftwareDefined Networks(SDN)introduced better network management by decoupling control and data plane.However,communication reliability is the desired property in computer networks.The frequency of communication link failure degrades network performance,and service disruptions are likely to occur.Emerging network applications,such as delaysensitive applications,suffer packet loss with higher Round Trip Time(RTT).Several failure recovery schemes have been proposed to address link failure recovery issues in SDN.However,these schemes have various weaknesses,which may not always guarantee service availability.Communication paths differ in their roles;some paths are critical because of the higher frequency usage.Other paths frequently share links between primary and backup.Rerouting the affected flows after failure occurrences without investigating the path roles can lead to post-recovery congestion with packet loss and system throughput.Therefore,there is a lack of studies to incorporate path criticality and residual path capacity to reroute the affected flows in case of link failure.This paper proposed Reliable Failure Restoration with Congestion Aware for SDN to select the reliable backup path that decreases packet loss and RTT,increasing network throughput while minimizing post-recovery congestion.The affected flows are redirected through a path with minimal risk of failure,while Bayesian probability is used to predict post-recovery congestion.Both the former and latter path with a minimal score is chosen.The simulation results improved throughput by(45%),reduced packet losses(87%),and lowered RTT(89%)compared to benchmarking works.

Evaluation of restoration success in arid rangelands of Iran based on the variation of ecosystem services

The plantation of non-native species is one of the most expensive ecological restoration measures in arid and semi-arid areas,while its impacts on local communities are largely ignored.This study assessed the rate of change and the dynamic degree of the economic values of ecosystem services related to local conservation(water yield,stocking rate and aesthetic value)and preserving the future(carbon sequestration,soil protection,soil stability and habitat provision)to determine the restoration success of the plantation of non-native species Haloxylon ammodendron(C.A.Mey.)Bunge ex Fenzl(15-and 30-year-old)in parts of arid rangelands of Bardsir region,Kerman Province,Iran.We investigated the impacts of the two plantations on the seven ecosystem services and ecosystem structures(horizontal and vertical structures,vegetation composition and species diversity)based on field sampling and measurements at four sampling sites(i.e.,control,degraded,and 15-and 30-year-old plantation sites)in spring and summer of 2022.The restoration success of the plantation of non-native species was then examined by assessing the rate of change and the dynamic degree of the total economic value of all ecosystem services as well as the rate of change and the dynamic degree of the economic values of ecosystem services for the two groups(local conservation and preserving the future).Although the plantation of non-native species H.ammodendron enormously improved the vertical and horizontal structures of ecosystems,it failed to increase species diversity and richness fully.Further,despite the plantation of non-native species H.ammodendron had significantly increased the economic values of all ecosystem services,it was only quite successful in restoring carbon sequestration.Path analysis showed that plantation age had a significant impact on restoration success directly and indirectly(through changing ecosystem structures and services).The dynamic degree of the economic values of ecosystem services related to local conservation and preserving the future at the 15-and 30-year-old plantation sites indicated that the two plantations successfully restored the ecosystem services related to preserving the future.The presented method can help managers select the best restoration practices and predict their ecological-social success,especially for the plantation of high-risk non-native species in arid and semi-arid areas.

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points(SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

Effects of Ecological Restoration of Mangrove Wetlands Using Native Mangrove Species to Replace Spartina alterniflora: A Case Study in Southern China

Within the expanse of China’s coastline, the invasive alien cordgrass species Spartina alterniflora has caused profound nationwide damage and has emerged as a critical factor contributing to the degradation of mangrove wetlands, especially in the study area in Beihai, Guangxi. However, current treatments for S. alterniflora remain less effective and limited research focuses on the preliminary changes after artificial plantation. A comprehensive approach combining physical interventions with biological control measures has been employed to eradicate smooth cordgrass and facilitate the restoration of native mangrove wetlands. The study involved the periodic monitoring of the growth conditions of mangroves and the biodiversity of avian and benthic organisms, conducted at three to four-month intervals following the artificial plantation with one-year-old seedlings and propagules of native mangrove species Rhizophora stylosa. Results indicated that through the allometric equation, the above-ground biomass of planted seedlings had a ~20 g increase in average but the growth conditions were not significant over an eight-month period. High percentage of important avian species underlined the potential of the study site to serve as a worthwhile habitat and notable seasonal variations were observed in the biodiversity of bird species. Biodiversity indices of bird and benthos species also followed a similar fluctuation and reached a peak in April 2023. This research underscores the initial lack of distinct improvements during the early stages of the ecological restoration project, thorough maintenance, long-term monitoring, holistic considerations on a larger scale would be imperative for ongoing projects in the future.

Exploration and preliminary clinical investigation of an adhesive approach for primary tooth restoration

The current study aims to investigate a suitable adhesive for primary tooth enamel. Shear bond strength(SBS)of primary teeth and the length of resin protrusion were analyzed using one-way ANOVA with Bonferroni multiple comparison tests after etching with 35% H_(3)PO_(4). SBS and marginal microleakage tests were conducted with Single Bond Universal(SBU)/Single Bond 2(SB2) adhesives with or without pre-etching using a nonparametric Kruskal-Wallis test. Clinical investigations were performed to validate the adhesive for primary teeth restoration using Chi-square tests. Results showed that the SBS and length of resin protrusion increased significantly with the etching time. Teeth in the SBU with 35% H_(3)PO_(4)pre-etching groups had higher bond strength and lower marginal microleakage than those in the SB2 groups. Mixed fractures were more common in the 35% H_(3)PO_(4)etched 30 s + SB2/SBU groups. Clinical investigations showed significant differences between the two groups in cumulative retention rates at the 6-, 12-and 18-month follow-up evaluations, as well as in marginal adaptation, discoloration, and secondary caries at the 12-and 18-month follow-up assessments.Together, pre-etching primary teeth enamel for 30 s before SBU treatment improved clinical composite resin restoration, which can provide a suitable approach for restoration of primary teeth.

Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.

Asymmetric Loss Based on Image Properties for Deep Learning-Based Image Restoration

Deep learning techniques have significantly improved image restoration tasks in recent years.As a crucial compo-nent of deep learning,the loss function plays a key role in network optimization and performance enhancement.However,the currently prevalent loss functions assign equal weight to each pixel point during loss calculation,which hampers the ability to reflect the roles of different pixel points and fails to exploit the image’s characteristics fully.To address this issue,this study proposes an asymmetric loss function based on the image and data characteristics of the image recovery task.This novel loss function can adjust the weight of the reconstruction loss based on the grey value of different pixel points,thereby effectively optimizing the network training by differentially utilizing the grey information from the original image.Specifically,we calculate a weight factor for each pixel point based on its grey value and combine it with the reconstruction loss to create a new loss function.This ensures that pixel points with smaller grey values receive greater attention,improving network recovery.In order to verify the effectiveness of the proposed asymmetric loss function,we conducted experimental tests in the image super-resolution task.The experimental results show that the model with the introduction of asymmetric loss weights improves all the indexes of the processing results without increasing the training time.In the typical super-resolution network SRCNN,by introducing asymmetric weights,it is possible to improve the peak signal-to-noise ratio(PSNR)by up to about 0.5%,the structural similarity index(SSIM)by up to about 0.3%,and reduce the root-mean-square error(RMSE)by up to about 1.7%with essentially no increase in training time.In addition,we also further tested the performance of the proposed method in the denoising task to verify the potential applicability of the method in the image restoration task.