Image Sharpening Research Articles

Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement.

The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions alongside delicate and fine-grained regions. Recent methods leverage neural radiance fields aided by predicted surface normal priors to recover the scene geometry. These methods excel in producing complete and smooth results for floor and wall areas. However, they struggle to capture complex surfaces with high-frequency structures due to the inadequate neural representation and the inaccurately predicted normal priors. This work aims to reconstruct high-fidelity surfaces with fine-grained details by addressing the above limitations. To improve the capacity of the implicit representation, we propose a hybrid architecture to represent low-frequency and high-frequency regions separately. To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors. Identifying such uncertainty can prevent our model from being misled by unreliable surface normal supervisions that hinder the accurate reconstruction of intricate geometries. Experiments on the benchmark datasets show that our method outperforms existing methods in terms of reconstruction quality. Furthermore, the proposed method also generalizes well to real-world indoor scenarios captured by our hand-held mobile phones. Our code is publicly available at: https://github.com/yec22/Fine-Grained-Indoor-Recon.

Research on detection method of photovoltaic cell surface dirt based on image processing technology

In view of the reduced power generation efficiency caused by ash or dirt on the surface of photovoltaic panels, and the problems of heavy workload and low efficiency faced by manual detection, this study proposes a method to detect dust or dust on the surface of photovoltaic cells with the help of image processing technology to timely eliminate hidden dangers and improve power generation efficiency.This paper introduces image processing methods based on mathematical morphology, such as image enhancement, image sharpening, image filtering and image closing operation, which makes the image better highlight the target to be recognized. At the same time, it also solves the problem of uneven image binarization caused by uneven illumination in the process of image acquisition. By using the image histogram equalization, the gray level concentration area of the original image is opened or the gray level is evenly distributed, so that the dynamic range of the pixel gray level is increased, so that the image contrast or contrast is increased, the image details are clear, to achieve the purpose of enhancement. When identifying the target area, the method of calculating the proportion of the dirt area to the whole image area is adopted, and the ratio exceeding a certain threshold is judged as a fault. In addition, the improved A* path planning algorithm is adopted in this study, which greatly improves the efficiency of the unmanned aerial vehicle detection of photovoltaic cell dirt, saves time and resources, reduces operation and maintenance costs, and improves the operation and maintenance level of photovoltaic units.

Neurofibroma detection using joint imaging genomics and ensemble learning

Accurate detection of neurofibromas is crucial for quantitative monitoring of tumor progression and surgical assessment. This paper proposes a method for neurofibroma detection in whole-body magnetic resonance imaging (WBMRI) using joint imaging genomics and ensemble learning. Firstly, we enhance texture features through a combination of image sharpening, filtering, brightness adjustment, and contrast enhancement. Then, we employ a weighted boxes fusion (WBF) technique based on test-time augmentation (TTA) under a single model and further integrate multiple models using the dual fusion approach of TTA and WBF. For segmentation, we utilize minimum bounding boxes based on segmentation masks for position calibration. Finally, false positive tumor regions are further eliminated through imaging genomics features. The experimental MRI data is obtained from collaboration between Harvard Medical School and domestic tertiary hospitals, comprising 158 cases with a total of 1380 tumors. Five-fold cross-validation is conducted with segmentation annotations completed by domain experts. Compared to the best results of single models, our proposed method achieves a 10.1% increase in average precision (AP), 7.8% increase in sensitivity, reduction of average false positives to 3.58, a decrease of 17.68, and an 8.5% improvement in competitive performance metric (CPM). This method effectively enhances the accuracy of neurofibroma detection and is applicable to detecting tumors and lesions in other medical imaging applications.

Neurofibroma detection using joint imaging genomics and ensemble learning

Accurate detection of neurofibromas is crucial for quantitative monitoring of tumor progression and surgical assessment. This paper proposes a method for neurofibroma detection in whole-body magnetic resonance imaging (WBMRI) using joint imaging genomics and ensemble learning. Firstly, we enhance texture features through a combination of image sharpening, filtering, brightness adjustment, and contrast enhancement. Then, we employ a weighted boxes fusion (WBF) technique based on test-time augmentation (TTA) under a single model and further integrate multiple models using the dual fusion approach of TTA and WBF. For segmentation, we utilize minimum bounding boxes based on segmentation masks for position calibration. Finally, false positive tumor regions are further eliminated through imaging genomics features. The experimental MRI data is obtained from collaboration between Harvard Medical School and domestic tertiary hospitals, comprising 158 cases with a total of 1380 tumors. Five-fold cross-validation is conducted with segmentation annotations completed by domain experts. Compared to the best results of single models, our proposed method achieves a 10.1% increase in average precision (AP), 7.8% increase in sensitivity, reduction of average false positives to 3.58, a decrease of 17.68, and an 8.5% improvement in competitive performance metric (CPM). This method effectively enhances the accuracy of neurofibroma detection and is applicable to detecting tumors and lesions in other medical imaging applications.

Analisis Perubahan Garis Pantai Pada Wilayah Pesisir Kecamatan Amurang Kabupaten Minahasa Selatan Sulawesi Selatan

Chandra, 2023 this study is to determine the extent of coastline changes in the coastal area of Amurang District, South Minahasa Regency and the causes of coastline changes that occur in the Coastal Area of Amurang District, South Minahasa Regency. Changes in coastline are determined by the amount of sediment that goes in and out of each section of the coast. If the incoming sediment is higher than the outgoing, then the beach will experience sedimentation on the contrary, if the incoming sediment is smaller than the outgoing, then the beach will experience erosion. This research was conducted in several stages, namely collecting data and information and analyzing data. Initial image processing consists of image provisioning, image recovery, image cropping, and image sharpening. Field surveys and supporting data collection, as well as advanced image processing which includes classification, supporting data processing, image overlay and interpretation of research results. Changes in the coastline in the coastal area of Amurang District in 5 years average -1.81 Ha, abrasion with a toal of -7.23 Ha and an average accretion of 1.47 Ha with a total of 5.8 Ha, and changes in the coastline in the coastal area of Amurang district are caused by two factors, namely hydro-oceanographic factors that cause abrasion while anthrophogenic factors cause accretion.

Defect identification of electricity transmission line insulators based on the improved lightweight network model with computer vision assistance

This work aims to ensure the safe operation of electricity transmission lines and reduce costs and maintenance difficulties. It studies the application of computer vision (CV) in the defect identification of electricity transmission lines. In addition, this work proposes a method to improve the lightweight network model to provide an effective identification model to solve the problem of electricity transmission line defects. Firstly, GraphCut segmentation and Laplace algorithms are employed to expand and sharpen the electricity transmission line image. Secondly, in light of the Depth Separable Convolution algorithm, a defect detection model for the electricity transmission line insulator is proposed based on the You Only Look Once 4 (YOLOv4) network. Moreover, MobileNetV1 is utilized to improve this lightweight network model. Finally, this work uses ImageNet, a large public dataset, to validate the proposed model experimentally. The research results reveal that: (1) In the model testing results, all research indicators of the model are greater than 90 %, indicating an excellent detection accuracy of this model. (2) The improved YOLOv4 model can increase the detection speed to 53 frames/s at the cost of 2.4 % accuracy. (3) After image sharpening, the improved YOLOv4 model has promoted the insulator defects' detection ability to a certain extent. The above outcomes suggest that the improved YOLOv4 model can predict more efficiently and accurately and reduce unnecessary false positives. This illustrates that the proposed model is feasible and is expected to be applied to the defect identification of electricity transmission lines in practice. These findings fully demonstrate this work's vital value in enhancing the prediction efficiency and accuracy, thus offering a strong preference for the defect identification of electricity transmission lines in practical applications.

Robust Self‐powered Optoelectronic Synapses Based on Epitaxial InSe/GaN Heterojunction with Interfacial Charge‐Trapping Layer

AbstractNeuromorphic devices that parallelize perception, preprocessing, and computation functions are expected to play a significant role in future non‐von Neumann architecture computers. Herein, a new retina‐inspired broadband self‐powered optoelectronic synaptic device based on 2D/3D heterojunction of epitaxial InSe on GaN(0001) is reported. Few‐layer n‐type InSe is grown on p‐type GaN by physical vapor deposition in an ultra‐high vacuum (UHV) environment. The devices are fabricated using a shadow mask assisted UHV electrode deposition technique. High‐resolution transmission electron microscopy images reveal that an atomically thin amorphous layer, which induces highly efficient charge trapping, is formed at the InSe/GaN interface. The photoresponse spans from visible to near‐infrared, and the response time is prolonged to 103 ms owing to the deep trapping levels. Thus, synaptic functions, including excitatory postsynaptic current, paired‐pulse facilitation with a high index of up to 170%, short‐term plasticity, and high‐pass filtering characteristics, are realized. Additionally, the synapses demonstrated the merit of realizing image sharpening and arithmetic operations on the same device under infrared and visible light illumination. This study provides a new platform of 2D/3D heterostructures for robust optoelectronic synapses that may find applications in post‐Moore era neuromorphic vision systems.

CattNIS: Novel identification system of cattle with retinal images based on feature matching method

Small and large farm animals are frequently identified using techniques like microchipping or tagging. However, these techniques may have a detrimental impact on animal welfare and may result in theft and fraud. Modern and sophisticated identification procedures using biometrics, including retina, face, and iris recognition, are available and have less of a detrimental effect on animal welfare. Because biometric markers are unchangeable and unaffected, it is feasible to stop situations of theft and fraud. This study uses retina images to identify animals based on each animal's different and specific retinal patterns. To the best of our knowledge, there isn't a publicly accessible dataset for images of animal retinas. The three main goals of this study are to: 1) produce a novel dataset of Cattle retinal images and make it publicly available; 2) create a successful image processing-based system for retinal identification and recognition; and 3) create a graphical user interface (GUI) that enables querying the database to ascertain whether a specific Cattle retina is present. To accomplish these goals, a dataset comprising 2430 images from the left and right eyes of 300 cattle was produced. After the dataset was created, the animal retinal images were segmented using image processing methods such as scaling, color transformations, image sharpening, contrast enhancements, noise filtering, and histogram equalization. By using the segmented images with the SURF, FAST, BRISK, and HARRIS techniques, distinctive retinal features were found. During the identification phase, the animal whose retinal characteristic matched the sought animal's retina the closest was recognized. Within the developed CattNIS system, the SURF approach has the highest accuracy rate of 92.25 percent. The results of this study show that the identification of cattle using retinal vascular patterns is highly successful. The availability of the collected cattle retinal images from this study is expected to significantly contribute to the advancement of future research in this field, both in terms of quantity and quality.

Underwater image sharpening and color correction via dataset based on revised underwater image formation model

Underwater image sharpening and color correction via dataset based on revised underwater image formation model

Deep Seabed Image Enhancement Algorithm for Manned Submersible Acquisition

Objective: Due to the problems of light propagation underwater, such as scattering and absorption, which leads to low contrast, color distortion and blurring of details in the images obtained underwater, this phenomenon is more serious in the deep sea, and most undersea map images collected by manned submersibles during deep-sea exploration exhibit these issues. To address these problems, an underwater image fusion enhancement algorithm based on color correction and image sharpening is proposed for image enhancement and restoration. Methods: The automatic color enhancement algorithm is used to correct and enhance the color of the underwater image. Subsequently, the RGB three channels are corrected by gamma filtering. The RGB space is then converted to the Lab space, and the L channel is processed by the Contrast Limited Adaptive Histogram Equalization algorithm to enhance the luminance. At the same time, the color-corrected image is image-sharpened by using unsharpened mask algorithms. Finally, image fusion is performed by using the algorithm based on the fusion of guided filters. Results: The experimental results on several underwater images under different scenarios show that the enhancement effect achieved by this algorithm is better than the comparison algorithm. In the subjective aspect, the color and details of the images are better balanced and enhanced. In the objective aspect, quantitative evaluations are carried out in three aspects: Information entropy (IE), underwater image quality measure (UIQM), and underwater color image quality evaluation (UCIQE), in which the IE is quantitatively evaluated. Regarding the UCIQE, the proposed algorithm has a better effect than other algorithms in terms of IE, UIQM, and UCIQE. Specifically, compared to the original image, the proposed algorithm shows improvements of 33.4%, 1.45 times, and 54.4% or more in mean value. Conclusion: The results show that the algorithm has good results in image processing for underwater environments, with enhancement and restoration of images acquired through manned submersibles.

Image Color Balance with Laplacian and Gaussian Pyramid (CBLGP) Algorithm

In recent times, technology has rapidly and significantly evolved across all sectors. Digital image processing stands out as a modern technology aimed at achieving clear images. However, digitized images often encounter issues of low quality, such as unclear or underwater images that require enhancement for better visibility. These problems stem from factors like deficient focusing, lighting, and various constraints leading to low contrast, shading, and artifacts. Underwater and satellite images consistently face less-than-ideal conditions due to environmental factors like light refraction in water, particle scattering, and dust in aquatic environments. Similarly, challenges in space, such as poor illumination and lack of contrast, further complicate image analysis. Overcoming these obstacles is crucial for extracting valuable information, necessitating advanced processing techniques. This paper introduces an enhanced Gaussian/Laplace color balance-fusion algorithm designed to improve image visibility. Modifications to certain equations result in sharper and clearer images. The algorithm begins by determining the white balance of the input RGB color image and subsequently enhances its intensity. Edge improvement is carried out separately using a depth filter. The weights for each image are then determined and combined to form a Laplace Pyramid. A color restoration technique is applied to process the resulting image, producing the final enhanced image. While existing methods for image contrast enhancement typically focus on image features, they often neglect user characteristics. This paper explores the application of image sharpening, a prominent image enhancement technique, in clearing underwater or low-quality images using the proposed algorithm. Keywords: EDSHE, High pass filter, White patch ratix, Laplacian pyramid, Color restoration.

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in the preprocessing step. The unsharp mask algorithm was used for image sharpening. Wiener and total variation denoising methods were used for image denoising. The image-fusion process was conducted in two steps: (1) fusing the spectral bands into one multispectral image and (2) pansharpening the panchromatic and multispectral images using the PanColorGAN model. The effectiveness of the proposed approach was evaluated using quantitative and qualitative assessment techniques, including no-reference image quality assessment (NR-IQA) metrics. In this experiment, the unsharp mask algorithm noticeably improved the spatial details of the pansharpened images. No preprocessing algorithm dramatically improved the color quality of the enhanced images. The proposed fusion approach improved the images without importing unnecessary blurring and color distortion issues.

Multimodal approach for early prediction of COVID-19 disease using convolutional neural network

<p><span>The latest human coronavirus is COVID-19. Chest radiography imaging is essential for screening, early detection, and monitoring COVID-19 infections since the virus resides in the lungs. Classical real time reverse transcriptase polymerase chain reaction (RT-PCR) data and chest X-ray pictures will become more important for COVID-19 identification as the pandemic spreads due to their affordability, wide availability, and infection control benefits, which reduce cross-contamination. This work presents multi-modal hybrid automated approaches to classify COVID-19 illness into three clinical categories: normal, pathogenic, and COVID-19 utilising RT-PCR test data and online chest X-ray datasets. The RT-PCR and chest X-ray image datasets were processed using supervised machine learning and convolutional neural networks (CNN). Together, these measures help us separate COVID-19 patients, those with similar symptoms, and healthy persons. The author improved detection times and classification accuracy with extra tree classifier’s feature selection and openCV’s image sharpening. The proposed approaches were tested using a research dataset. The proposed methods allowed reliable COVID-19 disease categorization for clinical decision-making, with random forest (RF) classifier global precision values of 91.58% on the RT-PCR dataset and CNN model accuracy of 95.46% on improved sharpened images.</span></p>

Spectral-Spatial Transformer for Hyperspectral Image Sharpening.

Convolutional neural networks (CNNs) have recently achieved outstanding performance for hyperspectral (HS) and multispectral (MS) image fusion. However, CNNs cannot explore the long-range dependence for HS and MS image fusion because of their local receptive fields. To overcome this limitation, a transformer is proposed to leverage the long-range dependence from the network inputs. Because of the ability of long-range modeling, the transformer overcomes the sole CNN on many tasks, whereas its use for HS and MS image fusion is still unexplored. In this article, we propose a spectral-spatial transformer (SST) to show the potentiality of transformers for HS and MS image fusion. We devise first two branches to extract spectral and spatial features in the HS and MS images by SST blocks, which can explore the spectral and spatial long-range dependence, respectively. Afterward, spectral and spatial features are fused feeding the result back to spectral and spatial branches for information interaction. Finally, the high-resolution (HR) HS image is reconstructed by dense links from all the fused features to make full use of them. The experimental analysis demonstrates the high performance of the proposed approach compared with some state-of-the-art (SOTA) methods.

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.

Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.

An underwater image enhancement model combining physical priors and residual network

AbstractDeep learning‐based methods have achieved notable performance for underwater image enhancement. However, previous studies are mostly focused on pursuing high similarity between the original image and the target, which incurs performance drop when the models are used for real‐world images. A new framework for underwater image enhancement is proposed to improve the generalization performance of enhancement. First, the coordinate attention module is integrated into the backbone network, which serves as a pre‐trained model, to strengthen the feature extraction capability of the network. Second, the backbone is finetuned by physical prior knowledge and real‐world images, in an unsupervised manner, to realize generalization from artificial images to real‐world images. Furthermore, a model protection mechanism is designed to guarantee the successful execution of the training. The experimental results indicate that the proposed method provides a powerful pre‐trained backbone network and the finetuning strategy can further solve the color distortion and improve the image sharpening, especially in the harsh real environment. Compared with relevant methods, the UCIQE and NIQE are, respectively, 0.525 and 4.149, with a 0.009–0.095 increase in UCIQE and a 0.256–1.032 decrease in NIQE compared to other methods.

A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening

Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands. Subsequently, an improved PCNN (IPCNN) is proposed, which considers the differences of the neighboring neurons. Furthermore, the Chameleon Swarm Optimization (CSA) optimization is adopted to generate the optimum fusion parameters for IPCNN. Hence, the injected spatial details are acquired in the irregular regions generated by the IPCNN. Extensive experiments are carried out to validate the superiority of the proposed model, which confirms that our method can realize hyperspectral imagery with high spatial resolution, yielding the best spatial details and spectral information among the state-of-the-art approaches. Several ablation studies further corroborate the efficiency of our method.

Dual-modal artificial synapse based on a lead-free halide Cu2AgBiI6 for image processing and information encryption

The lead-free perovskite-inspired materials are regarded as one of the alternatives to lead-based perovskite for the preparation of neuromorphic devices due to their low toxicity, excellent optical and transport properties. Herein, a two-terminal artificial synapse was fabricated based on a novel lead-free halide Cu2AgBiI6 thin film. The fabricated device exhibited the versatile synaptic behavior whether subjected to external electrical or optical stimulation benefitting from the excellent photoelectric characteristics of Cu2AgBiI6. Furthermore, the intrinsic photovoltaic feature of Cu2AgBiI6 enabled the device with capabilities of operating in a self-powered mode under optical stimulation. The device successfully imitated the retina’s capacity for color perception by employing optical pulses of various wavelengths. The dependence of the device response on the optical pulse frequency allowed it to function as a high-pass filter for image sharpening. At last, the device was applied for the information encryption and decryption transmission based on a constructed password book including 128 common characters and numbers, which shows great potential for information encryption and decryption transmission. The comprehensive characters of the device display great superiority over other artificial synaptic devices.