Images Of Different Resolutions Research Articles

Blood flow in deforming vessels

Blood flow is most often modeled in vessels whose shape does not change over time. This approach significantly simplifies the calculations and reduces their time. Temporal vascular deformations affect blood flow patterns, the deposition of atherosclerotic plaques, the formation of aneurysms, and many other phenomena. There are two methods to consider blood vessel deformations: registration of vascular geometry by diagnostic imaging such as CT, MRI, ultrasound or the use of the fluid-structure interaction (FSI) models. The first method requires additional procedures for processing image data, and the second requires knowledge of the material properties of the vessel walls, which is a very challenging task for in vivo experiments. The article will show the method to obtain a time-varying geometry of the coronary artery based on images obtained by angioCT. Emerging difficulties arise from the use of images of different resolutions and the need to generate numerical grids of the same topology at every time step. This problem was solved by using image segmentation, smoothing the resulting 3D geometry, and projecting the numerical grid on subsequent shapes of the vessel using diffeomorphism. The simulations have been validated using measurements on a phantom. The resulting velocity fields show the locations of intense oscillations of the shear stress of the wall where the atherosclerotic plaque is deposited.

Selected aspects of blood flow simulations in arteries

This article discusses selected aspects of modelling blood flow in the arteries. The method of reproducing the variable-in-time geometry of coronary arteries is given based on a sequence of medical images of different resolutions. Within the defined shapes of the arteries, a technique of generation of numerical meshes of the same topology is described. The boundary conditions and non-Newtonian rheological models used in blood flow are discussed, as well as the description of blood as a multiphase medium. The work also includes a discussion of tests on the phantom of the carotid artery for the accuracy of measurements made using ultrasonography.

Hyperspectral and multispectral image fusion via residual selective kernel attention-based U-net

ABSTRACT The fusion of low-resolution hyperspectral image (LR-HSI) and high-resolution multispectral image (HR-MSI) is a crucial technology for producing high-resolution hyperspectral images. Most existing image fusion algorithms based on deep learning do not fully utilize the ability of neural network to extract and process multi-scale features, which leads to the problem of difficulty in fully learning features and ambiguity of features. In order to overcome these issues, a residual selective kernel attention-based U-net named RSKAU-net is designed for LR-HSI and HR-MSI fusion. RSKAU-net is constructed by a residual selective kernel module with an attention mechanism and a channel attention block. The residual selective kernel attention-based (RSKA) module is designed to process images of different resolutions, which adaptively extracts multi-scale features and efficiently emphasizes significant features through the attention mechanism. The channel attention (CA) module retains important spectral information by assigning different weights to each channel of LR-HSI. The proposed network can enhance the spatial information of LR-HSI while preserving its spectral information. Meanwhile, it effectively fuses the features from the source image to obtain the HR-HSI with rich details. The experimental results demonstrate that the proposed network has advantages in terms of both visual effect and objective quantitative indices when compared to existing HSI-MSI fusion approaches.

Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT

Due to the difficulties in retrieving both the time-dependent shapes of the vessels and the generation of numerical meshes for such cases, most of the simulations of blood flow in the cardiac arteries use static geometry. The article describes a methodology for generating a sequence of time-dependent 3D shapes based on images of different resolutions and qualities acquired from ECG-gated coronary artery CT angiography. The precision of the shape restoration method has been validated using an independent technique. The original proposed approach also generates for each of the retrieved vessel shapes a numerical mesh of the same topology (connectivity matrix), greatly simplifying the CFD blood flow simulations. This feature is of significant importance in practical CFD simulations, as it gives the possibility of using the mesh-morphing utility, minimizing the computation time and the need of interpolation between boundary meshes at subsequent time instants. The developed technique can be applied to generate numerical meshes in arteries and other organs whose shapes change over time. It is applicable to medical images produced by other than angio-CT modalities.

Evaluating the Efficacy of Segment Anything Model for Delineating Agriculture and Urban Green Spaces in Multiresolution Aerial and Spaceborne Remote Sensing Images

Segmentation of Agricultural Remote Sensing Images (ARSIs) stands as a pivotal component within the intelligent development path of agricultural information technology. Similarly, quick and effective delineation of urban green spaces (UGSs) in high-resolution images is also increasingly needed as input in various urban simulation models. Numerous segmentation algorithms exist for ARSIs and UGSs; however, a model with exceptional generalization capabilities and accuracy remains elusive. Notably, the newly released Segment Anything Model (SAM) by META AI is gaining significant recognition in various domains for segmenting conventional images, yielding commendable results. Nevertheless, SAM’s application in ARSI and UGS segmentation has been relatively limited. ARSIs and UGSs exhibit distinct image characteristics, such as prominent boundaries, larger frame sizes, and extensive data types and volumes. Presently, there is a dearth of research on how SAM can effectively handle various ARSI and UGS image types and deliver superior segmentation outcomes. Thus, as a novel attempt in this paper, we aim to evaluate SAM’s compatibility with a wide array of ARSI and UGS image types. The data acquisition platform comprises both aerial and spaceborne sensors, and the study sites encompass most regions of the United States, with images of varying resolutions and frame sizes. It is noteworthy that the segmentation effect of SAM is significantly influenced by the content of the image, as well as the stability and accuracy across images of different resolutions and sizes. However, in general, our findings indicate that resolution has a minimal impact on the effectiveness of conditional SAM-based segmentation, maintaining an overall segmentation accuracy above 90%. In contrast, the unsupervised segmentation approach, SAM, exhibits performance issues, with around 55% of images (3 m and coarser resolutions) experiencing lower accuracy on low-resolution images. Whereas frame size exerts a more substantial influence, as the image size increases, the accuracy of unsupervised segmentation methods decreases extremely fast, and conditional segmentation methods also show some degree of degradation. Additionally, SAM’s segmentation efficacy diminishes considerably in the case of images featuring unclear edges and minimal color distinctions. Consequently, we propose enhancing SAM’s capabilities by augmenting the training dataset and fine-tuning hyperparameters to align with the demands of ARSI and UGS image segmentation. Leveraging the multispectral nature and extensive data volumes of remote sensing images, the secondary development of SAM can harness its formidable segmentation potential to elevate the overall standard of ARSI and UGS image segmentation.

Diabetic Retinopathy Image Classification Using Shift Window Transformer

Diabetic retinopathy is one of the most dangerous complications for diabetic patients, leading to blindness if not diagnosed early. However, early diagnosis can control and prevent the disease from progressing to blindness. Transformers are considered state-of-the-art models in natural language processing that do not use convolutional layers. In transformers, means of multi-head attention mechanisms capture long-range contextual relations between pixels. For grading diabetic retinopathy, CNNs currently dominate deep learning solutions. However, the benefits of transformers, have led us to propose an appropriate transformer-based method to recognize diabetic retinopathy grades. A major objective of this research is to demonstrate that the pure attention mechanism can be used to determine diabetic retinopathy and that transformers can replace standard CNNs in identifying the degrees of diabetic retinopathy. In this study, a Swin Transformer-based technique for diagnosing diabetic retinopathy is presented by dividing fundus images into nonoverlapping batches, flattening them, and maintaining positional information using a linear and positional embedding procedure. Several multi-headed attention layers are fed into the resulting sequence to construct the final representation. In the classification step, the initial token sequence is passed into the SoftMax classification layer, which produces the recognition output. This work introduced the Swin transformer performance on the APTOS 2019 Kaggle for training and testing using fundus images of different resolutions and patches. The test accuracy, test loss, and test top 2 accuracies were 69.44%, 1.13, and 78.33%, respectively for 160*160 image size, patch size=2, and embedding dimension C=64. While the test accuracy was 68.85%, test loss: 1.12, and test top 2 accuracy: 79.96% when the patch size=4, and embedding dimension C=96. And when the size image is 224*224, patch size=2, and embedding dimension C=64, the test accuracy: 72.5%, test loss: 1.07, and test top 2 accuracy: 83.7%. When the patch size =4, embedding dimension C=96, the test accuracy was 74.51%, test loss: 1.02, and the test top 2 accuracy was 85.3%. The results showed that the Swin Transformer can achieve flexible memory savings. The proposed method highlights that an attention mechanism based on the Swin Transformer model is promising for the diabetic retinopathy grade recognition task.

Superpixel-Based Style Transfer Method for Single-Temporal Remote Sensing Image Identification in Forest Type Groups

Forests are the most important carbon reservoirs on land, and forest carbon sinks can effectively reduce atmospheric CO2 concentrations and mitigate climate change. In recent years, various satellites have been launched that provide opportunities for identifying forest types with low cost and high time efficiency. Using multi-temporal remote sensing images and combining them with vegetation indices takes into account the vegetation growth pattern and substantially improves the identification accuracy, but it has high requirements for imaging, such as registration, multiple times, etc. Sometimes, it is difficult to satisfy, the plateau area is severely limited by the influence of clouds and rain, and Gaofen (GF) data require more control points for orthophoto correction. The study area was chosen to be Huize County, situated in Qujing City of Yunnan Province, China. The analysis was using the GF and Landsat images. According to deep learning and remote sensing image feature extraction methods, the semantic segmentation method of F-Pix2Pix was proposed, and the domain adaptation method according to transfer learning effectively solved the class imbalance in needleleaf/broadleaf forest identification. The results showed that (1) this method had the best performance and a higher accuracy than the existing products, 21.48% in non-forest/forest and 29.44% in needleleaf/broadleaf forest for MIoU improvement. (2) Applying transfer learning domain adaptation to semantic segmentation showed significant benefits, and this approach utilized satellite images of different resolutions to solve the class imbalance problem. (3) It can be used for long-term monitoring of multiple images and has strong generalization. The identification of needleleaf and broadleaf forests combined with the actual geographical characteristics of the forest provides a foundation for the accurate estimation of regional carbon sources/sinks.

Cross-Attention Based Multi-Resolution Feature Fusion Model for Self-Supervised Cervical OCT Image Classification.

Cervical cancer seriously endangers the health of the female reproductive system and even risks women's life in severe cases. Optical coherence tomography (OCT) is a non-invasive, real-time, high-resolution imaging technology for cervical tissues. However, since the interpretation of cervical OCT images is a knowledge-intensive, time-consuming task, it is tough to acquire a large number of high-quality labeled images quickly, which is a big challenge for supervised learning. In this study, we introduce the vision Transformer (ViT) architecture, which has recently achieved impressive results in natural image analysis, into the classification task of cervical OCT images. Our work aims to develop a computer-aided diagnosis (CADx) approach based on a self-supervised ViT-based model to classify cervical OCT images effectively. We leverage masked autoencoders (MAE) to perform self-supervised pre-training on cervical OCT images, so the proposed classification model has a better transfer learning ability. In the fine-tuning process, the ViT-based classification model extracts multi-scale features from OCT images of different resolutions and fuses them with the cross-attention module. The ten-fold cross-validation results on an OCT image dataset from a multi-center clinical study of 733 patients in China indicate that our model achieved an AUC value of 0.9963 ± 0.0069 with a 95.89 ± 3.30% sensitivity and 98.23 ± 1.36 % specificity, outperforming some state-of-the-art classification models based on Transformers and convolutional neural networks (CNNs) in the binary classification task of detecting high-risk cervical diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer. Furthermore, our model with the cross-shaped voting strategy achieved a sensitivity of 92.06% and specificity of 95.56% on an external validation dataset containing 288 three-dimensional (3D) OCT volumes from 118 Chinese patients in a different new hospital. This result met or exceeded the average of four medical experts who have used OCT for over one year. In addition to promising classification performance, our model has a remarkable ability to detect and visualize local lesions using the attention map of the standard ViT model, providing good interpretability for gynecologists to locate and diagnose possible cervical diseases.

A Real-Time FPGA Implementation of Infrared and Visible Image Fusion Using Guided Filter and Saliency Detection.

Taking advantage of the functional complementarity between infrared and visible light sensors imaging, pixel-level real-time image fusion based on infrared and visible light images of different resolutions is a promising strategy for visual enhancement, which has demonstrated tremendous potential for autonomous driving, military reconnaissance, video surveillance, etc. Great progress has been made in this field in recent years, but the fusion speed and quality of visual enhancement are still not satisfactory. Herein, we propose a multi-scale FPGA-based image fusion technology with substantially enhanced visual enhancement capability and fusion speed. Specifically, the source images are first decomposed into three distinct layers using guided filter and saliency detection, which are the detail layer, saliency layer and background layer. Fusion weight map of the saliency layer is subsequently constructed using attention mechanism. Afterwards weight fusion strategy is used for saliency layer fusion and detail layer fusion, while weight average fusion strategy is used for the background layer fusion, followed by the incorporation of image enhancement technology to improve the fused image contrast. Finally, high-level synthesis tool is used to design the hardware circuit. The method in the present study is thoroughly tested on XCZU15EG board, which could not only effectively improve the image enhancement capability in glare and smoke environments, but also achieve fast real-time image fusion with 55FPS for infrared and visible images with a resolution of 640 × 470.

Developing a victorious strategy to the second strong gravitational lensing data challenge

ABSTRACT Strong lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with deep learning have become a popular approach due to these astronomical objects’ rarity and image complexity. Next-generation surveys will provide more opportunities to derive science from these objects and an increasing data volume to be analysed. However, finding strong lenses is challenging, as their number densities are orders of magnitude below those of galaxies. Therefore, specific strong lensing search algorithms are required to discover the highest number of systems possible with high purity and low false alarm rate. The need for better algorithms has prompted the development of an open community data science competition named strong gravitational lensing challenge (SGLC). This work presents the deep learning strategies and methodology used to design the highest scoring algorithm in the second SGLC (II SGLC). We discuss the approach used for this data set, the choice of a suitable architecture, particularly the use of a network with two branches to work with images in different resolutions, and its optimization. We also discuss the detectability limit, the lessons learned, and prospects for defining a tailor-made architecture in a survey in contrast to a general one. Finally, we release the models and discuss the best choice to easily adapt the model to a data set representing a survey with a different instrument. This work helps to take a step towards efficient, adaptable, and accurate analyses of strong lenses with deep learning frameworks.

An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on <uri>https://github.com/DearCaat/ioplin</uri>, and the CQU-BPDD dataset is able to be accessed on <uri>https://dearcaat.github.io/CQU-BPDD/</uri>.

ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision

• Deep learning method for automated assessment of plumage condition in laying hens. • Novel architecture for simultaneous hen detection and plumage score prediction. • RGB and RGB-D images in different resolutions were evaluated as input. • Possibility of automated plumage assessment in commercial laying hen farms was demonstrated. Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive, manual task. This study proposes a novel approach for automated plumage condition assessment using computer vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of input image characteristics, the method was evaluated using images with and without depth information in resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of subjective human annotations, plumage condition predictions were compared to manual assessments of one observer and to matching annotations of two observers. Among all tested settings, performance metrics based on matching manual annotations of two observers were equal or better than the ones based on annotations of a single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of 98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of plumage conditions in commercial laying hen farms.

Rich Structural Index for Stereoscopic Image Quality Assessment.

The human visual system (HVS), affected by viewing distance when perceiving the stereo image information, is of great significance to study of stereoscopic image quality assessment. Many methods of stereoscopic image quality assessment do not have comprehensive consideration for human visual perception characteristics. In accordance with this, we propose a Rich Structural Index (RSI) for Stereoscopic Image objective Quality Assessment (SIQA) method based on multi-scale perception characteristics. To begin with, we put the stereo pair into the image pyramid based on Contrast Sensitivity Function (CSF) to obtain sensitive images of different resolution. Then, we obtain local Luminance and Structural Index (LSI) in a locally adaptive manner on gradient maps which consider the luminance masking and contrast masking. At the same time we use Singular Value Decomposition (SVD) to obtain the Sharpness and Intrinsic Structural Index (SISI) to effectively capture the changes introduced in the image (due to distortion). Meanwhile, considering the disparity edge structures, we use gradient cross-mapping algorithm to obtain Depth Texture Structural Index (DTSI). After that, we apply the standard deviation method for the above results to obtain contrast index of reference and distortion components. Finally, for the loss caused by the randomness of the parameters, we use Support Vector Machine Regression based on Genetic Algorithm (GA-SVR) training to obtain the final quality score. We conducted a comprehensive evaluation with state-of-the-art methods on four open databases. The experimental results show that the proposed method has stable performance and strong competitive advantage.

Micro-Expression Recognition Based on Pixel Residual Sum and Cropped Gaussian Pyramid

Facial micro-expression(ME) recognition has great significance for the progress of human society and could find a person's true feelings. Meanwhile, ME recognition faces a huge challenge, since it is difficult to detect and easy to be disturbed by the environment. In this article, we propose two novel preprocessing methods based on Pixel Residual Sum. These methods can preprocess video clips according to the unit pixel displacement of images, resist environmental interference, and be easy to extract subtle facial features. Furthermore, we propose a Cropped Gaussian Pyramid with Overlapping(CGPO) module, which divides images of different resolutions through Gaussian pyramids and crops different resolutions images into multiple overlapping subplots. Then, we use a convolutional neural networks of progressively increasing channels based on the depthwise convolution to extract preliminary features. Finally, we fuse preliminary features and make position embedding to get the last features. Our experiments show that the proposed methods and model have better performance than the well-known methods.

Vision Transformer‐based recognition of diabetic retinopathy grade

In the domain of natural language processing, Transformers are recognized as state-of-the-art models, which opposing to typical convolutional neural networks (CNNs) do not rely on convolution layers. Instead, Transformers employ multi-head attention mechanisms as the main building block to capture long-range contextual relations between image pixels. Recently, CNNs dominated the deep learning solutions for diabetic retinopathy grade recognition. However, spurred by the advantages of Transformers, we propose a Transformer-based method that is appropriate for recognizing the grade of diabetic retinopathy. The purposes of this work are to demonstrate that (i) the pure attention mechanism is suitable for diabetic retinopathy grade recognition and (ii) Transformers can replace traditional CNNs for diabetic retinopathy grade recognition. This paper proposes a Vision Transformer-based method to recognize the grade of diabetic retinopathy. Fundus images are subdivided into non-overlapping patches, which are then converted into sequences by flattening, and undergo a linear and positional embedding process to preserve positional information. Then, the generated sequence is input into several multi-head attention layers to generate the final representation. The first token sequence is input to a softmax classification layer to produce the recognition output in the classification stage. The dataset for training and testing employs fundus images of different resolutions, subdivided into patches. We challenge our method against current CNNs and extreme learning machines and achieve an appealing performance. Specifically, the suggested deep learning architecture attains an accuracy of 91.4%, specificity=0.977 (95% confidence interval (CI) (0.951-1)), precision=0.928 (95% CI (0.852-1)), sensitivity=0.926 (95% CI (0.863-0.989)), quadratic weighted kappa score=0.935, and area under curve (AUC)=0.986. Our comparative experiments against current methods conclude that our model is competitive and highlight that an attention mechanism based on a Vision Transformer model is promising for the diabetic retinopathy grade recognition task.

Face and Ethnical Group Recognition with Images of Different Resolutions

In this paper, a dataset for Xinjiang minority ethnical groups is introduced, and implementation of two dimensional Linear Discriminant Analysis (2DLDA) and two-dimensional Partial Least Squares (2DPLS) is investigated. Two important topics for face recognition and the ethnicity recognition are investigated for database with different image resolutions. Experiments show that 2DLDA performances better than 2DPLS on our face database.

FDPPGAN: remote sensing image fusion based on deep perceptual patchGAN

Remote sensing satellites can simultaneously capture high spatial resolution panchromatic (PAN) images and low spatial resolution multispectral (MS) images. Pan-sharpening in the fusion of remote sensing images aims to generate high-resolution MS images by integrating the spatial information of PAN images and the spectral characteristics of MS images. In this study, a novel deep perceptual patch generative adversarial network (FDPPGAN) was proposed to solve the pan-sharpening problem. First, a perception generator was constructed, it included, a matching module, which can process as input images of different resolutions, a fusion module, a reconstruction module based on the residual structure, and a module for the extracting perceptual features. Second, patch discriminator was utilized to convert the dichotomy of the sample into that multiple partial images of the same size to ensure that the generated results can retain more detailed features. Finally, the loss function of FDPPGAN comprised perceptual feature loss, content loss, generator loss, and discriminator loss. Experiments on the QuickBird and WorldView datasets demonstrated that the proposed algorithm is superior to state-of-the-art algorithms in subjective and objective indexes.

Different Input Resolutions and Arbitrary Output Resolution: A Meta Learning-Based Deep Framework for Infrared and Visible Image Fusion.

Infrared and visible image fusion has gained ever-increasing attention in recent years due to its great significance in a variety of vision-based applications. However, existing fusion methods suffer from some limitations in terms of the spatial resolutions of both input source images and output fused image, which prevents their practical usage to a great extent. In this paper, we propose a meta learning-based deep framework for the fusion of infrared and visible images. Unlike most existing methods, the proposed framework can accept the source images of different resolutions and generate the fused image of arbitrary resolution just with a single learned model. In the proposed framework, the features of each source image are first extracted by a convolutional network and upscaled by a meta-upscale module with an arbitrary appropriate factor according to practical requirements. Then, a dual attention mechanism-based feature fusion module is developed to combine features from different source images. Finally, a residual compensation module, which can be iteratively adopted in the proposed framework, is designed to enhance the capability of our method in detail extraction. In addition, the loss function is formulated in a multi-task learning manner via simultaneous fusion and super-resolution, aiming to improve the effect of feature learning. And, a new contrast loss inspired by a perceptual contrast enhancement approach is proposed to further improve the contrast of the fused image. Extensive experiments on widely-used fusion datasets demonstrate the effectiveness and superiority of the proposed method. The code of the proposed method is publicly available at https://github.com/yuliu316316/MetaLearning-Fusion.

THE STUDY OF THE CURRENT STATE OF THE SOIL COVER OF THE AKSHAT RURAL COUNTY OF WEST KAZAKHSTAN REGION ON THE BASIS OF GIS TECHNOLOGIES

The rational use and protection of soils in market conditions requires adequate application of new scientific and methodological approaches. One of such systematic-analytical methods of soil cadastre organizations is a combination of traditional terrestrial methods with technologies of geoinformation systems (GIS) based on extensive use of satellite images in different resolutions. The aggregate of information necessary for mapping soil cover patterns and their quantification has been described in GIS databases. Data integration has been realized through the spatial and attributive component in the form of: the results of topographic and thematic maps. At the same time, the creation of attributive GIS databases involves the digitization of thematic maps tied into a single cartographic projection (as a topographic map with a scale of 1: 50 000). As a result of the study, thematic maps and attributive databases of GIS of soils were formed. As a result of research, based on GIS technology, a digital soil map of the Akshat rural county of the Chingirlau district of the West Kazakhstan region has been developed using the ArcGIS software product.

The image inpainting algorithm used on multi-scale generative adversarial networks and neighbourhood

Various problems existed in the image inpainting algorithms, which can’t meet people's requirements visually. Aiming at the defects of the existing image inpainting algorithms, such as low accuracy, poor visual consistency, and unstable training, an improved image inpainting algorithm used on a multi-scale generative adversarial network (GAN) and neighbourhood model have been proposed in the paper. The proposed algorithm mainly improves the network structure of the discriminator, and introduces a multi-scale discriminator based on the global discriminator and the local discriminator. The multi-scale discriminators were trained on images of different resolutions. Discriminators of different scales have different receptive fields, which can guide the generator to generate more global image views and finer details. Aiming at the problem of gradient disappearance or gradient explosion that often occurs in GAN training, the method of WGAN (Wasserstein GAN) has been used to simulate the sample data distribution using EM distance. The proposed model has been trained and tested on the CelebA, ImageNet, and Place2. The experimental results show that compared with the previous algorithm model, the proposed algorithm improves the accuracy of image inpainting and can generate more realistic repairing images, and it is suitable for many types of images.