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Journal articles on the topic 'Methods of image processing'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Mohamed Y Abdallah, Yousif, Mohamed MO Yousef, and Eltayeb W Eltayeb. "Automated Enhancement of Myocardium Images using Image Processing Methods." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 557–64. https://doi.org/10.21275/sr21709185141.

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Kotsiubivska, Kateryna, and Viktoria Tymoshenko. "Mathematical Methods of Image Processing." Digital Platform: Information Technologies in Sociocultural Sphere 2, no. 1 (2019): 41–54. https://doi.org/10.31866/2617-796x.2.1.2019.175653.

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The purpose of the study is to study the specificity of image encoding by spline interpolation, and the equation of the indicated method with other mathematical methods of encoding and image processing. Research methods. The mathematical and algorithmic models and methods of solving the problem of smoothing on the basis of spline approximation, as well as the possibility of using an appropriate mathematical apparatus for encoding and image processing. The novelty of the research is the isolation of the compression algorithm of images based on the methods of spline approximation. This

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Shambetova, Burul, and Mekia Shigute Gaso. "ANALYSIS OF EDGE DETECTION METHODS IN IMAGE PROCESSING." Alatoo Academic Studies 23, no. 2 (2023): 519–26. http://dx.doi.org/10.17015/aas.2023.232.50.

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The primary goal of computer vision is to interpret the contents of an image, which can be achieved through image segmentation. This technique involves dividing an image into meaningful regions based on the intended application. By detecting and outlining the edges of objects, we can identify them within the image. Edges refer to the boundaries between objects and the background, as well as the boundaries between overlapping objects. Through image segmentation, we can separate the image from the background and extract valuable information. Edge detection is a crucial step in image segmentation

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Davies, W. S. "Digital image processing methods." Optics and Lasers in Engineering 21, no. 4 (1994): 250–51. http://dx.doi.org/10.1016/0143-8166(94)90076-0.

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DJEBALI, M., M. MELKEMI, K. MELKEMI, and N. SAPIDIS. "COIFLET BASED METHODS FOR RANGE IMAGE PROCESSING." International Journal of Image and Graphics 07, no. 02 (2007): 321–51. http://dx.doi.org/10.1142/s0219467807002672.

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In industry applications, the range images are generally huge points arrays and are additively noised. They usually represent surfaces of 3D objects and are used for reverse engineering process in CAD/CAM domains. To compute the geometrical model of each surface present in the range image, we denoise and sub-sample the raw range data. Denoising allows us to avoid the adverse effects of the noise on the obtained result. Sub-sampling the raw range data leads to a low image processing overheads like those of segmentation process. Based on interpolation properties of particular wavelets named coif

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Patil, Varsha, Deepali Sale, and M. A. Joshi. "Image Fusion Methods and Quality Assessment Parameters." Asian Journal of Engineering and Applied Technology 2, no. 1 (2013): 40–45. http://dx.doi.org/10.51983/ajeat-2013.2.1.643.

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Image processing techniques primarily focus upon enhancing the quality of an image or a set of images and to derive the maximum information from them. Image Fusion is such a technique of producing a superior quality image from a set of available images. It is the process of combining relevant information from two or more images into a single image wherein the resulting image will be more informative and complete than any of the input images. A lot of research is being done in this field encompassing areas of Computer Vision, Automatic object detection, Image processing, parallel and distribute

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Kotsiubivska, Kateryna, and Viktoria Tymoshenko. "Mathematical Methods of Image Processing." Digital Platform: Information Technologies in Sociocultural Sphere 2, no. 1 (2019): 41–54. http://dx.doi.org/10.31866/2617-796x.2.1.2019.175653.

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Radescu, Teodor-Adrian, and Arpad Gellert. "Fog Detection through Image Processing Methods." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 13, no. 1 (2023): 28–37. http://dx.doi.org/10.2478/ijasitels-2023-0004.

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Abstract This paper presents a fog detection algorithm, highlighting the significance of continued exploration in fog identification through image processing techniques. The advancement and application of this algorithm can significantly benefit various domains, including road safety, environmental monitoring, navigation, security, surveillance, and improving existing systems’ performance. The evaluation performed on test images have shown an accuracy of 72%, a precision of 94%, a recall of 57% and an F1 score of 0.71. The proposed algorithm clearly outperformed some existing fog detection met

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Bakhshi, Ali, Kobra Hajizadeh, Mohammad Reza Tanhayi, and Reza Jamshidi. "Diabetic retinopathy diagnosis using image processing methods." Advances in Obesity, Weight Management & Control 11, no. 5 (2022): 132–34. http://dx.doi.org/10.15406/aowmc.2022.12.00375.

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Diabetes mellitus is common disease nowadays which could cause blindness. Earlier detection of diabetes signs from retina fundus images could help predicting and preventing the damages. Image processing methods could process the matrix data of pictures as blood vessel segmentation and exudate detection. In this research, the CLAHE algorithm with morphological transformations are used to blood vessel segmentation and determination of the Hessian matrix of images are utilized to detect the exudate blobs

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Chulichkov, Alexey I., and Dmitriy A. Balakin. "Measurement Reduction Methods for Processing Tomographic Images." Sensors 23, no. 2 (2023): 563. http://dx.doi.org/10.3390/s23020563.

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The importance of development of new methods for reconstruction of an object image given its sinogram and some additional information about the object stems from the possibility of artifact presence in the reconstructed image, or its insufficient sharpness when the used additional information does not hold. The problem of recovering artifact-free images of the studied object from tomography data is considered in the framework of the theory of computer-aided measuring systems. Methods for solving it are developed. They are based on narrowing the class of possible images using less artifact-indu

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Tang, Kaiyi, Shuangyang Zhang, Zhichao Liang, et al. "Advanced Image Post-Processing Methods for Photoacoustic Tomography: A Review." Photonics 10, no. 7 (2023): 707. http://dx.doi.org/10.3390/photonics10070707.

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Photoacoustic tomography (PAT) is a promising imaging technique that utilizes the detection of light-induced acoustic waves for both morphological and functional biomedical imaging. However, producing high-quality images using PAT is still challenging and requires further research. Besides improving image reconstruction, which turns the raw photoacoustic signal into a PAT image, an alternative way to address this issue is through image post-processing, which can enhance and optimize the reconstructed PAT image. Image post-processing methods have rapidly emerged in PAT and are proven to be esse

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Kara, Caner, Mehmet Ali Yalçınkaya, Enes Açıkgözoğlu, and Ayhan Arısoy4. "Multifunctional Image Enhancement Tool with Image Processing Methods." Journal of Software Engineering and Simulation 10, no. 9 (2024): 01–08. http://dx.doi.org/10.35629/3795-10090108.

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The success of image-based machine learning and deep learning algorithms depends on the amount and variety of data sets. The richness and heterogeneity of the dataset has a direct impact on the capacity of the model to generalize to new, previously unknown data. Obtaining a large number and variety of images is a very difficult and laborious process, especially for practical applications to be developed in areas such as health, industry and agriculture. If this difficulty cannot be overcome, the performance of machine learning models may decrease or may not reach the desired level at all. This

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Huang, Chaoyan, Juncheng Li, and Guangwei Gao. "Review of Quaternion-Based Color Image Processing Methods." Mathematics 11, no. 9 (2023): 2056. http://dx.doi.org/10.3390/math11092056.

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Images are a convenient way for humans to obtain information and knowledge, but they are often destroyed throughout the collection or distribution process. Therefore, image processing evolves as the need arises, and color image processing is a broad and active field. A color image includes three distinct but closely related channels (red, green, and blue (RGB)). Compared to directly expressing color images as vectors or matrices, the quaternion representation offers an effective alternative. There are several papers and works on this subject, as well as numerous definitions, hypotheses, and me

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Rathore, Gurpreet, and Vijay Dhir. "A comparative approach to image registration methods." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 6, no. 2 (2013): 757–62. http://dx.doi.org/10.24297/ijmit.v6i2.3821.

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Image processing methods are possibly able to visualize objects inside the human body. Efficient image processing methods are useful in medical diagnosis, treatment planning and medical research. Medical images are used for medical diagnosis. These images should be geometrically aligned for better observation. Registration is necessary technique to integrate data taken from different measurements. Image Registration is a process of overlaying two or more images that can taken at different times, using different devices, different viewpoints and from different angles in order to have 2D or 3D p

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Legland, David, and Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab." F1000Research 10 (April 30, 2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab env

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Progonov, Dmytro O., and Volodymyr M. Lutsenko. "Effectiveness of stego images pre-processing withspectral analysis methods." Applied Aspects of Information Technology 5, no. 1 (2022): 64–75. http://dx.doi.org/10.15276/aait.05.2022.6.

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Early detection of sensitive data leakage during message transmission in communication systems is topical task today. This is complicated by applying of attackers to advanced steganographic methods. Feature of such methods is sensitive information embedding into innocuous (cover) files, such as digital images. This drastically reduces effectiveness of modern stegdetectors based on applying of signature and statistical steganalysis methods. There are proposed several approaches for improving detection accuracy of stegdetectors that are based on image pre-processing(calibration). These methods a

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17

Eesa, Aseel Muslim, and Hayder Raaid Talib. "Comparison of the Methods of Image Slicing After Initial Image Processing Using the Statistical Confidence Limits Technique." Annals of Pure and Applied Mathematics 24, no. 01 (2021): 53–64. http://dx.doi.org/10.22457/apam.v24n1a06838.

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The use of image segmentation in image processing is of great importance in analyzing and extracting information from images, and one of the most important segmentation techniques is the threshold technique, which is considered one of the simplest techniques of image division in image processing. The statistical methods play an important role in the process of image segmentation. Statistical confidence in image processing, preliminary processing, as it removed noise from the images, and here the obscure noise was used. After that, the resulting images were cut, the initial processing process w

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18

Marchuk, V. I., A. I. Okorochkov, V. V. Semenov, I. A. Sadrtdinov, and I. O. Nikishin. "Diagnostics of materials by diffraction optical methods." Industrial laboratory. Diagnostics of materials 88, no. 3 (2022): 23–28. http://dx.doi.org/10.26896/1028-6861-2022-88-3-23-28.

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The internal state of the material formed as a result of technological processing, indirectly affects the state of the material surface. A non-contact method of non-destructive control of the state of materials based on a visual analysis of the surface, requires high-quality images which can be obtained either using lens objectives or lenseless technologies. The results of studying image processing obtained by lensless technologies are presented. We used methods for modeling phase masks and image processing based on Gerchberg – Saxton iterative algorithms, adaptive-additive and phase mask rota

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19

Liubchenko, Vira, and Dmytro Moroz. "Models and methods of object detection in digital image processing." Bulletin of the National Technical University "KhPI" A series of "Information and Modeling" 1, no. 1-2 (11-12) (2024): 61–74. http://dx.doi.org/10.20998/2411-0558.2024.01.06.

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Object detection in digital images can face such problems that arise in the process of image registration as the presence of noise, low image quality/resolution, illumination heterogeneity, overlapping objects, and others. These problems complicate the process of object detection and lead to errors in image processing algorithms. To solve these problems, we study the advantages, disadvantages, and technical features of models and methods for detecting objects in digital images for their reasonable selection in practical applications. Figs. 2. Refs. 11 titles.

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Abdulhamid, Mohanad, and Lwanga Wanjira. "Image Processing Techniques Based Crowd Size Estimation." Radioelectronics. Nanosystems. Information Technologies 12, no. 3 (2020): 407–14. http://dx.doi.org/10.17725/rensit.2020.12.407.

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Image processing algorithms are the basis for image computer analysis and machine Vision. Employing a theoretical foundation, image algebra, and powerful development tools, Visual C++, Visual Fortran, Visual Basic, and Visual Java, high-level and efficient computer vision techniques have been developed. This paper analyzes different image processing algorithms by classifying them in logical groups. In addition, specific methods are presented illustrating the application of such techniques to the real world images. In most cases more than one method is used. This allows a basis for comparison o

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Pan, Ming-Sie, Chao-Hsing Fan, Ching-Chao Yang, et al. "Fingerprint preprocessing using FCN and U-net methods." Journal of Information and Optimization Sciences 45, no. 5 (2024): 1421–34. http://dx.doi.org/10.47974/jios-1731.

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This paper analyzes the importance of optimizing fingerprint images from the perspective of front-line forensics personnel. Due to defects such as fingerprint ridge blurring and fingerprint image overlap, it may cause delays in fingerprint identification and lead to misjudgments (false positives, false negatives) and other consequences. This paper simulated common fingerprint images (fuzzy feature points) at criminal cases, and captured, segmented, and reconstructed fingerprints. Two deep learning model architectures, U-net [1] and FCN [2], were applied to realize image segmentation and image

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22

Abhijeet, A. Patil* S. P. Patil. "A REVIEW OF IMAGE MOSAICING METHODS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 941–46. https://doi.org/10.5281/zenodo.52505.

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Image Mosaicing is the process of combining two or more images of the same scene into one image and generate panorama of high resolution image. In this paper, we have described the basic methods used to generate panorama image. Our objective is to provide different methods and algorithms used to generate panoramic image. Mosaicing is one of the techniques of image processing which is useful for tiling digital images. Mosaicing is blending together of several arbitrarily shaped images to form one large radiometrically balanced image so that the boundaries between the original images are not see

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23

Guo, Wei, Shi Hai Zhao, and Xian Bao Wang. "An Image-Based Processing of Wool Fiber Fineness Improved Measurement Methods." Applied Mechanics and Materials 513-517 (February 2014): 4223–26. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4223.

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Firstly, the development of a general image processing program , and the image processing program , select a certain number of wool fibers MATLAB2010 SEM images of image processing software platform programming experiment to achieve clear image contains a description of the fiber diameter index , and based on the processed in previous studies, the diameter of the image measurement method based on the improved measurement methods and a detailed description of the experiment show that the method can greatly improve the recognition accuracy of wool fibers .

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24

Triana, Juan, and Luis Ferro. "Finite difference methods in image processing." Selecciones Matemáticas 8, no. 02 (2021): 411–16. http://dx.doi.org/10.17268/sel.mat.2021.02.17.

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25

Angenent, Sigurd, Eric Pichon, and Allen Tannenbaum. "Mathematical methods in medical image processing." Bulletin of the American Mathematical Society 43, no. 03 (2006): 365–97. http://dx.doi.org/10.1090/s0273-0979-06-01104-9.

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26

Klein, Renata, Eyal Masad, Eduard Rudyk, and Itai Winkler. "Bearing diagnostics using image processing methods." Mechanical Systems and Signal Processing 45, no. 1 (2014): 105–13. http://dx.doi.org/10.1016/j.ymssp.2013.10.009.

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27

Popov, Alexander. "Analysis of Image Processing Methods in the Context of a Basis for Recognizing Small Objects. Image processing methods for object recognition." Computer tools in education, no. 1 (April 20, 2025): 33–47. https://doi.org/10.32603/2071-2340-2025-1-33-47.

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The article analyzes the existing methods of detecting small objects from images in the channels of technical vision against a background of noise. It is shown that the method of detecting images of small objects based on the calculation of the likelihood ratio using the estimation of the mathematical expectation of samples of spatially subband vectors and their covariance matrices is promising for further research. To solve the problem of detecting small objects from images of technical vision channels, as well as to prepare data for subsequent stages (recognition and identification), a subba

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Kokoshkin, Alexander V., Evgeny P. Novichikhin, and Ilia V. Smolyaninov. "Application of spectral and spatial processing methods to sonar images." Radioelectronics. Nanosystems. Information Technologies. 13, no. 3 (2021): 377–82. http://dx.doi.org/10.17725/rensit.2021.13.377.

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The paper proposes the use of the method of renormalization with limitation (MRL) for suppressing the speckle noise of images obtained using sonar. The method is tested on real images obtained by the interferometric side-view sonar. The principal possibility of a significant reduction in the speckle noise level is found due to the fact that the MRL renormalizes the spectrum of the sonar image to the universal reference spectrum (URS) model, which is a model of the spectrum of a "good" quality grayscale image. To increase the overall sharpness of the image, after applying the MRL, it is propose

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Rao, P. Hema, and Badri Vishal Padamwar. "Mathematical Methods in Image Processing: A Survey." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 3 (2019): 1654–59. http://dx.doi.org/10.61841/turcomat.v10i3.14626.

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Image processing is a fundamental field that plays a crucial role in various applications such as medical imaging, remote sensing, and computer vision. Mathematical methods form the foundation of image processing algorithms, providing the framework for understanding image properties and designing efficient processing techniques. This survey provides a comprehensive overview of the mathematical methods used in image processing, including digital image representation, image enhancement, restoration, and segmentation. Advanced mathematical methods such as sparse representations, variational metho

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Raukhvarger, Alexey Borisovich, and Pavel Alekseevich Durandin. "BRIGHTNESS-CONTRAST MAGNIFIER ALGORITHM AND METHODS OF ITS ENHANCING." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2020, no. 4 (2020): 29–37. http://dx.doi.org/10.24143/2072-9502-2020-4-29-37.

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The paper considers the algorithmic basis of the developed application, which allows the user to select image fragments for viewing not only in enlarged form, but also with increased detail distinctness through brightness-contrast transformations. There has been proposed an algorithm of upsizing and processing the selected image fragment according to the required parameters of average brightness and contrast. The advantages of the proposed method of image processing in comparison with global methods for processing the entire image are investigated. The considered approach develops the advantag

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Progonov, Dmytro Oleksandrovych, and Volodymyr Mykolayovych Lucenko. "Effectiveness of stego images pre-processing with spectral analysis methods." Applied Aspects of Information Technology 5, no. 1 (2022): 64–75. http://dx.doi.org/10.15276/aait.01.2022.6.

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Early detection of sensitive data leakage during message transmission in communication systems is topical task today. This is complicated by applying of attackers to advanced steganographic methods. Feature of such methods is sensitive information embedding into innocuous (cover) files, such as digital images. This drastically reduces effectiveness of modern stegdetectors based on applying of signature and statistical steganalysis methods. There are proposed several approaches for improving detection accuracy of stegdetectors that are based on image pre-processing(calibration). These methods a

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32

Kavitha Soppari, Pakide Kavya, Kotla Pranay Teja, and Bethi Pavan Sai. "A survey on image captioning methods." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3134–43. https://doi.org/10.30574/wjarr.2025.26.2.1705.

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Image captioning is a task that Involves Natural Language Processing concepts to recognize the context of an image and describe them in a natural language like English. It requires good knowledge of Deep learning. Python, working on Jupyter notebooks, Keras library, Numpy, and Natural language processing It is a Python based project where we will use deep learning techniques of Convolutional Neural Networks and a type of Recurrent Neural Network (LSTM) together. The biggest challenge is most definitely being able to create a description that must capture not only the objects contained in an im

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Zekriti, Najat, Fatima Majid, Hachimi Taoufik, et al. "Improvement of crack tip position estimation in DIC images by image processing methods." Frattura ed Integrità Strutturale 17, no. 63 (2022): 61–71. http://dx.doi.org/10.3221/igf-esis.63.06.

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The study presents and compares an application of two procedures to identify the crack tip location in PVC Sent samples under a uniaxial tensile test based on the image processing method. An IDS camera captures several photos of the PVC surface as part of the image analysis procedure. All relevant data on crack initiation and propagation is collected and assessed using ImageJ software using image processing methods for detecting cracks. However, the second procedure involves a developed algorithm detecting the discontinuity using digital image correlation (DIC) measurement. Although, because o

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Strauss, Lourens Jochemus, and William ID Rae. "Image quality dependence on image processing software in computed radiography." South African Journal of Radiology 16, no. 2 (2012): 44–48. http://dx.doi.org/10.4102/sajr.v16i2.305.

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Background. Image post-processing gives computed radiography (CR) a considerable advantage over film-screen systems. After digitisation of information from CR plates, data are routinely processed using manufacturer-specific software. Agfa CR readers use MUSICA software, and an upgrade with significantly different image appearance was recently released: MUSICA2. Aim. This study quantitatively compares the image quality of images acquired without post-processing (flatfield) with images processed using these two software packages. Methods. Four aspects of image quality were evaluated.

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Huang, Kejia, and Yuxue Liu. "Research Progress in Image Dehazing Methods." Highlights in Science, Engineering and Technology 131 (March 25, 2025): 44–49. https://doi.org/10.54097/0y99jx69.

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Haze in the environment, caused by airborne particles, reduces image clarity and poses challenges for subsequent in-depth analysis and processing of images. Therefore, it is imperative to evaluate the advantages and disadvantages of various image dehazing methods, identify the challenges they face, and explore prospects. This paper focuses on image dehazing techniques, discussing the latest research advancements with an emphasis on comparing deep learning-based and traditional methods. It delves into the principles and application domains of different dehazing approaches, including image enhan

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E.Boopathi, Kumar &. Dr. V.Thiagarasu. "SEGMENTATION USING MASKING METHODS IN COLOUR IMAGES: AN APPROACH." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 2 (2017): 104–10. https://doi.org/10.5281/zenodo.268693.

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Image segmentation is one of the popular methods in the field of Image processing. It is the process of grouping an image into units that are consistent with respect to one or more characteristics. Segmentation in gray images has lots of methods and it has several algorithms to represent it. But images giving more information in scenes i.e., colour images have few numbers of methods to segment. So, this paper represent colour image segmentation methods in the literature and getting to prepare novel segmentation method with combined form of masking, thresholding and noise removal methods. Otsu

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Abousalem, Zib ziab. "3D from 2D for Nano images using images processing methods." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 2 (2014): 5437–47. http://dx.doi.org/10.24297/ijct.v14i2.2064.

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The scanning electron microscope (SEM) remains a main tool for semiconductor and polymer physics but TEM and AFM are increasingly used for minimum size features which called nanomaterials. In addition some physical properties such as microhardness, grain boundaries and domain structure are observed from optical and polarizing microscope which gives poor information and consequentially the error probability of discussion will be high.Thus it is natural to squeeze out every possible bit of resolution in the SEM, optical and polarizing microscopes for the materials under test. In our paper we wil

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Abousalem, Zib ziab. "3D from 2D for Nano images using images processing methods." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 2 (2014): 5437–47. http://dx.doi.org/10.24297/ijct.v14i2.2065.

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The scanning electron microscope (SEM) remains a main tool for semiconductor and polymer physics but TEM and AFM are increasingly used for minimum size features which called nanomaterials. In addition some physical properties such as microhardness, grain boundaries and domain structure are observed from optical and polarizing microscope which gives poor information and consequentially the error probability of discussion will be high. Thus it is natural to squeeze out every possible bit of resolution in the SEM, optical and polarizing microscopes for the materials under test. In our paper we wi

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Gu, Mengjie, and Qingtao Wu. "Review of Medical Image Segmentation Methods." Frontiers in Computing and Intelligent Systems 3, no. 3 (2023): 88–91. http://dx.doi.org/10.54097/fcis.v3i3.8573.

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Medical image is one of the key factors in the process of medical diagnosis and treatment. By analyzing the medical image obtained, doctors make judgments on the patient's condition and plan the next treatment process. Medical image segmentation is the process of segmenting areas of interest in medical images according to specific needs, which is a key step in medical image processing and analysis. With the great improvement of computer processing power, how to quickly and effectively segment huge image data and mining valuable information is the research hotspot of segmentation algorithm at p

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Rogayah, Rogayah, Waliya Rahmawanti, and Nur Azizah. "Colour-Based Extraction Methods for the Classification of Breast Milk (ASI)." CCIT Journal 14, no. 1 (2021): 21–27. http://dx.doi.org/10.33050/ccit.v14i1.966.

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The development of cellular devices makes accessing information in the form of text or images more easier. In line with the growing field of computer vision, various processes in image/image processing continue to increase. Image processing can be done by increasing image quality (image enhancement) and image recovery (image restoration). Feature extraction is divided into three types, namely feature form extraction, texture feature extraction, and color feature extraction. The application of color-based feature extraction methods has been widely used by researchers in the process of classific

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Wang, Xi, Taizheng Chen, Dongwei Li, and Shiqi Yu. "Processing Methods for Digital Image Data Based on the Geographic Information System." Complexity 2021 (June 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/2319314.

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Digital image data processing is mainly to input digital image data into a computer to complete the conversion of a continuous spatially distributed image model into a discrete digital model so that the computer can identify, process, and store the processing process of digital image information. Geographic information system (GIS) is a computer system that integrates multiple forms of information expression, and it integrates functions such as collection, processing, transmission, storage, management, analysis, expression, and query retrieval, which can quickly discover the spatial distributi

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Gądek-Moszczak, Aneta, Leszek Wojnar, and Adam Piwowarczyk. "Comparison of Selected Shading Correction Methods." System Safety: Human - Technical Facility - Environment 1, no. 1 (2019): 819–26. http://dx.doi.org/10.2478/czoto-2019-0105.

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AbstractShade effect is a defect of the images very often invisible for human vision perception but may cause difficulties in proper image processing and object detection especially if the aim of the task is to proceed detection and quantitative analysis of the objects. There are several methods in image processing systems or presented in the literature, however some of them introduce unexpected changes in the images, what may interfere the final quantitative analysis. In order to solve this problem, authors proposed a new method for shade correction, which is based on simulation of the image

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Nurgul, Uzakkyzy, Ismailova Aisulu, Ayazbaev Talgatbek, et al. "Image noise reduction by deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6855–61. https://doi.org/10.11591/ijece.v13i6.pp6855-6861.

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Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In

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Ulkar Huseynova, Anakhanim Mutallimova, Ulkar Huseynova, Anakhanim Mutallimova. "DIGITAL IMAGE PROCESSING." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 36, no. 01 (2024): 179–88. http://dx.doi.org/10.36962/pahtei36012024-179.

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Digital processing and subsequent picture identification are one of the scientific fields that is now experiencing rapid development. Currently, a lot of technology is focused on developing systems that use graphical images as information, including receiving, processing, storing, and transmitting information. Two primary areas of use for digital image processing methods are of interest: 1. Increasing image quality to enhance human visual perception. 2. Image processing for use in autonomous machine vision systems, including storage, transmission, and presentation. The fundamentals of digital

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LEE, SUH-YIN, and MAN-KWAN SHAN. "ACCESS METHODS OF IMAGE DATABASE." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (1990): 27–44. http://dx.doi.org/10.1142/s0218001490000034.

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The perception of spatial relationships among objects in a picture is one of the important selection criteria to discriminate and retrieve images in an image database system. The data structure called 2-D string, proposed by Chang et al., is adopted to represent the symbolic pictures. When there are a large number of images in the image database and each image contains many objects, the processing time for image retrievals is tremendous. It is essential to develop efficient access methods for these retrievals. In this paper, the efficient methods for retrieval by objects, retrieval by pairwise

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Uzakkyzy, Nurgul, Aisulu Ismailova, Talgatbek Ayazbaev, et al. "Image noise reduction by deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6855. http://dx.doi.org/10.11591/ijece.v13i6.pp6855-6861.

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<span lang="EN-US">Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the i

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Vidya Sagar Appaji, Setti. "Restoration of Human Face using Basic Image Processing Methods in MATLAB." International Journal of Science and Research (IJSR) 12, no. 5 (2023): 1617–20. http://dx.doi.org/10.21275/sr23513093450.

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Malathi, M., and P. Sinthia. "An Advanced Image Processing Prototype for Corrosion Finding Using Image Processing." Journal of Computational and Theoretical Nanoscience 18, no. 4 (2021): 1251–55. http://dx.doi.org/10.1166/jctn.2021.9388.

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The main objective of the research work is to recognize the rust of the substance with the help of Image Processing. The recognition of the rust portion of an image is carried out by quantizing of image in matrix form. The quantization process helps to perform the fundamental operation on image and also helps to identify the desired oxidation portion of an image. The corrosion portion was identified through the threshold operation, edge detection and segmentation. Threshold value assists to describe the types of the rust. Further the abrupt modification of colour in the images was captured by

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Ren, Xu Hu, and Jin Qiang Bai. "Research Based on Template Matching Technologys Sandbox Sand Body CT Image Processing Methods." Applied Mechanics and Materials 385-386 (August 2013): 1488–94. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1488.

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Based on analyzing and discussing the sand body CT fault image features in tectonic physical simulation experimental sand box, some technical schemes and methods of sand body CT image processing are proposed. By comparing the results of different image enhancement algorithms, image filtering algorithms for sand body CT image processing, the processing accuracy and efficiency of these methods are analyzed. On this basis, aiming at the characteristics of sand body CT sequence images, such as blurs, poor quality and obscure boundary between the layers, an improved template matching algorithm is p

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Saxena, Khushboo, and Yogesh Kumar Gupta. "Analysis of Image Processing Strategies Dedicated to Underwater Scenarios." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (2023): 253–58. http://dx.doi.org/10.17762/ijritcc.v11i3s.6232.

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Underwater images undergo quality degradation issues of an image, like blur image, poor contrast, non-uniform illumination etc. Therefore, to process these degraded images, image processing come into existence. In this paper, two important image processing methods namely Image restoration and Image enhancement are compared. This paper also discusses the quality measures parameters of image processing which will be helpful to see clear images.

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