Artículos de revistas: "Image processin"

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Jung, Sun Ho. "Study on CCTV based Sensor applied SURF Image Processin System". KOREA SCIENCE & ART FORUM 16 (30 de junio de 2014): 403. http://dx.doi.org/10.17548/ksaf.2014.06.16.403.

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Bhuvaneswari, M. "Gaussian mixture model: An application to parameter estimation and medical image classification". Journal of Scientific and Innovative Research 5, n.º 3 (25 de junio de 2016): 100–105. http://dx.doi.org/10.31254/jsir.2016.5308.

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Gaussian mixture model based parameter estimation and classification has recently received great attention in modelling and processin g data. Gaussian Mixture Model (GMM) is the probabilistic model for representing the presence of subpopulations and it works well with the classification and parameter estimation strategy. Here in this work Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques. The performance of the classification strategy of both the classifiers used has a better proficiency when compared to the other classifiers. By combining the SVM and GMM we co uld be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques. In this the GMM classifier and the SVM classifier are trained usin g the parameters and they are to be compared.

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Legland, David y Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab". F1000Research 10 (30 de abril de 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 environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

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Pariyar, Kopila. "Process of Detecting Barcodes Using Image Processing". International Journal of Scientific Engineering and Research 2, n.º 7 (27 de julio de 2014): 18–19. https://doi.org/10.70729/j2013326.

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Oguma, Ryuichiro, Syo Matsumura y Tetsuo Eguchi. "Mesoscopic TDGL Model for Formation of Domain Structures in D0₁₉ Type Ordering". Solid State Phenomena 172-174 (junio de 2011): 602–7. http://dx.doi.org/10.4028/www.scientific.net/ssp.172-174.602.

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The present authors recently presented the time-dependent Ginzburg-Landau (TDGL) formulation for L12 type ordering processin binary alloys, taking into account the symmetrical relationships of these ordered phases. Extending the formulation, the authors have developed the TDGL model for microstructural evolution of D019 type ordering. The D019 structure based on hcp is divided into four equivalent sublattices. The site occupation probabilities are given as a function of three order parameters and a composition parameter. Multiple types of variants of the structures are represented by the order parameters. Mean-field free energies are defined in a form of Landau type expansion with the order parameters and the composition parameter. Interfacial energies due to local variations of degrees of order and composition are given in a gradient square approximation. Kinetic equations for time-evolution of the order parameters and the composition one are derived from the Ginzburg-Landau type potential consisting of the mean-field free energies and the interfacial energy terms. Three-dimensional numerical simulations based on the kinetic equations have been performed, and the domain structures obtained are compared with a TEM image of Cu3Sn alloy.

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Mohamed Y Abdallah, Yousif, Mohamed MO Yousef y Eltayeb W Eltayeb. "Automated Enhancement of Myocardium Images using Image Processing Methods". International Journal of Science and Research (IJSR) 10, n.º 7 (27 de julio de 2021): 557–64. https://doi.org/10.21275/sr21709185141.

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Patel, Bindiya, Dr Pankaj Kumar Mishra y Prof Amit Kolhe. "Lung Cancer Detection on CT Images by using Image Processing". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 de abril de 2018): 2525–31. http://dx.doi.org/10.31142/ijtsrd11674.

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x, Priyanka. "Review Paper on Identifying Fake Images by Digital Image Processing". International Journal of Scientific Engineering and Research 5, n.º 5 (27 de mayo de 2017): 169–71. https://doi.org/10.70729/ijser151486.

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Tetard, Martin, Ross Marchant, Giuseppe Cortese, Yves Gally, Thibault de Garidel-Thoron y Luc Beaufort. "Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow". Climate of the Past 16, n.º 6 (2 de diciembre de 2020): 2415–29. http://dx.doi.org/10.5194/cp-16-2415-2020.

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Abstract. Identification of microfossils is usually done by expert taxonomists and requires time and a significant amount of systematic knowledge developed over many years. These studies require manual identification of numerous specimens in many samples under a microscope, which is very tedious and time-consuming. Furthermore, identification may differ between operators, biasing reproducibility. Recent technological advances in image acquisition, processing and recognition now enable automated procedures for this process, from microscope image acquisition to taxonomic identification. A new workflow has been developed for automated radiolarian image acquisition, stacking, processing, segmentation and identification. The protocol includes a newly proposed methodology for preparing radiolarian microscopic slides. We mount eight samples per slide, using a recently developed 3D-printed decanter that enables the random and uniform settling of particles and minimizes the loss of material. Once ready, slides are automatically imaged using a transmitted light microscope. About 4000 specimens per slide (500 per sample) are captured in digital images that include stacking techniques to improve their focus and sharpness. Automated image processing and segmentation is then performed using a custom plug-in developed for the ImageJ software. Each individual radiolarian image is automatically classified by a convolutional neural network (CNN) trained on a Neogene to Quaternary radiolarian database (currently 21 746 images, corresponding to 132 classes) using the ParticleTrieur software. The trained CNN has an overall accuracy of about 90 %. The whole procedure, including the image acquisition, stacking, processing, segmentation and recognition, is entirely automated via a LabVIEW interface, and it takes approximately 1 h per sample. Census data count and classified radiolarian images are then automatically exported and saved. This new workflow paves the way for the analysis of long-term, radiolarian-based palaeoclimatic records from siliceous-remnant-bearing samples.

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Arti Verma. "Digital Image Processing of SEM image of Polymer Nanocomposite Thin Film Using Java Based Program Image J." Power System Technology 43, n.º 2 (30 de junio de 2019): 44–46. https://doi.org/10.52783/pst.1135.

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Various images obtain from TEM; SEM can be analyzed with the help of computer image analysis software. Image processing is used to describe the size, shape, surface topography of micro or nano structure materials. In the present paper the characterization analysis is being reported qualitatively by using digital image processing of SEM/TEM image of some nanomaterials. This novel technique is an effective experimental tool for the detailed structural characterization. For image processing Java based software ImageJ is used in the present study.

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Goto, Mitsunori. "8. Image Processing Using ImageJ". Japanese Journal of Radiological Technology 75, n.º 7 (2019): 688–92. http://dx.doi.org/10.6009/jjrt.2019_jsrt_75.7.688.

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Dhiman Surender Kumar, Babita. "Analysis of Echocardiography Images Using Applications of Image Processing Techniques: A Review". International Journal of Scientific Engineering and Research 2, n.º 5 (27 de mayo de 2014): 34–38. https://doi.org/10.70729/j20132921.

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Strauss, Lourens Jochemus y William ID Rae. "Image quality dependence on image processing software in computed radiography". South African Journal of Radiology 16, n.º 2 (12 de junio de 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. An aluminium step-wedge was imaged using constant mA at tube voltages varying from 40 to 117kV. Signal-to-noise ratios (SNRs) and contrast-to-noise Ratios (CNRs) were calculated from all steps. Contrast variation with object size was evaluated with visual assessment of images of a Perspex contrast-detail phantom, and an image quality figure (IQF) was calculated. Resolution was assessed using modulation transfer functions (MTFs). Results. SNRs for MUSICA2 were generally higher than the other two methods. The CNRs were comparable between the two software versions, although MUSICA2 had slightly higher values at lower kV. The flatfield CNR values were better than those for the processed images. All images showed a decrease in CNRs with tube voltage. The contrast-detail measurements showed that both MUSICA programmes improved the contrast of smaller objects. MUSICA2 was found to give the lowest (best) IQF; MTF measurements confirmed this, with values at 3.5 lp/mm of 10% for MUSICA2, 8% for MUSICA and 5% for flatfield. Conclusion. Both MUSICA software packages produced images with better contrast resolution than unprocessed images. MUSICA2 has slightly improved image quality than MUSICA.

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Peng, Er Bao y Guo Tong Zhang. "Image Processing Technology Research of On-Line Thread Processing". Advanced Materials Research 908 (marzo de 2014): 555–58. http://dx.doi.org/10.4028/www.scientific.net/amr.908.555.

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The paper introduced image processing technology based on image segmentation about on-line threads images, and describes in detail image processing technology from image preprocessing, image gmentation,and threaded parameter test. Threaded images of on-line processing parts obtained are introduced as the key technology, Target edge extraction process from the segmented image are also recounted. At last, this article shows a comparison between actual machining parameters of screw thread and the standard parameter , provides the criterion for error compensation.

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Li, Xinrui, Yau Tsz Chan y Yangzi Jiang. "Development of an image processing software for quantification of histological calcification staining images". PLOS ONE 18, n.º 10 (5 de octubre de 2023): e0286626. http://dx.doi.org/10.1371/journal.pone.0286626.

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Quantification of the histological staining images gives important insights in biomedical research. In wet lab, it is common to have some stains off the target to become unwanted noisy stains during the generation of histological staining images. The current tools designed for quantification of histological staining images do not consider such situations; instead, the stained region is identified based on assumptions that the background is pure and clean. The goal of this study is to develop a light software named Staining Quantification (SQ) tool which could handle the image quantification job with features for removing a large amount of unwanted stains blended or overlaid with Region of Interest (ROI) in complex scenarios. The core algorithm was based on the method of higher order statistics transformation, and local density filtering. Compared with two state-of-art thresholding methods (i.e. Otsu’s method and Triclass thresholding method), the SQ tool outperformed in situations such as (1) images with weak positive signals and experimental caused dirty stains; (2) images with experimental counterstaining by multiple colors; (3) complicated histological structure of target tissues. The algorithm was developed in R4.0.2 with over a thousand in-house histological images containing Alizarin Red (AR) and Von Kossa (VK) staining, and was validated using external images. For the measurements of area and intensity in total and stained region, the average mean of difference in percentage between SQ and ImageJ were all less than 0.05. Using this as a criterion of successful image recognition, the success rate for all measurements in AR, VK and external validation batch were above 0.8. The test of Pearson’s coefficient, difference between SQ and ImageJ, and difference of proportions between SQ and ImageJ were all significant at level of 0.05. Our results indicated that the SQ tool is well established for automatic histological staining image quantification.

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Rahayu, Andriyati, Asril pramutadi Andi mustari y Baliana Amir. "Analisis Image Processing pada Prasasti Teroksidasi Ayam Téas I". PURBAWIDYA: Jurnal Penelitian dan Pengembangan Arkeologi 12, n.º 2 (29 de noviembre de 2023): 206–15. http://dx.doi.org/10.55981/purbawidya.2023.741.

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The Ayam Téas I inscription is one of the ancient inscriptions in Indonesia. Currently, the condition of the inscription has undergone natural degradation, causing the letters and the written message to become more difficult to read. Among the natural forms of degradation are corrosion and erosion. One method that can be used to address this problem is by utilizing image processing technology in the form of imageJ software. The analysis process involves capturing images using a camera and then processing the images using imageJ software. This software provides a mode that can remove unnecessary colors due to lighting, allowing some of the writings on the Ayam Téas I inscription to become more visible. Keywords: imageJ; prasasti; Ayam Téas I; histogram; grayscale

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Duan Jiazhen, 段家振, 史如新 Shi Ruxin, 张伟 Zhang Wei, 王庆 Wang Qing y 任显铭 Ren Xianming. "基于图像处理的碳钢表面激光除锈工艺研究". Chinese Journal of Lasers 51, n.º 20 (2024): 2002208. http://dx.doi.org/10.3788/cjl231563.

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Deshpande, Aadit, Sundaresan Raman, Amber Dubey, Pradeep Susvar y Rajiv Raman. "An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization". PLOS ONE 18, n.º 4 (21 de abril de 2023): e0283929. http://dx.doi.org/10.1371/journal.pone.0283929.

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Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.

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Yi, Wang, Zhang Jing y Gao Shuang. "Hue–saturation–intensity and texture feature-based cloud detection algorithm for unmanned aerial vehicle images". International Journal of Advanced Robotic Systems 17, n.º 3 (1 de mayo de 2020): 172988142090353. http://dx.doi.org/10.1177/1729881420903532.

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There are a large number of cloud-covered areas in most unmanned aerial vehicle images and lead to the loss of information in the image and affect image post procession such as image fusion and target identification. Finding the cloud-occluded area in an image is a key step in image processing. Based on the differences of color and texture characteristics between cloud and ground, a cloud detection algorithm for the unmanned aerial vehicle images is proposed. Simulation results show that the proposed algorithm is better than the classical cloud detection algorithms in accuracy rate, false-positive rate, and kappa coefficient.

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Maria Riasat. "Research on various image processing techniques". Open Access Research Journal of Chemistry and Pharmacy 1, n.º 1 (30 de diciembre de 2021): 005–12. http://dx.doi.org/10.53022/oarjcp.2021.1.1.0029.

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Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that can perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms and gives an image as an output. The most common example is Adobe Photoshop. It is one of the widely used applications for processing digital images. The image processing techniques play a vital role in image Acquisition, image pre-processing, Clustering, Segmentation, and Classification techniques with different kinds of images such as Fruits, Medical, Vehicle, and Digital text images, etc. In this study, the various images remove unwanted noise and performance enhancement techniques such as contrast limited adaptive histogram equalization.

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Laing, Ronald A. "Image Processing of Corneal Endothelial Images". Cornea 6, n.º 1 (1987): 65. http://dx.doi.org/10.1097/00003226-198706010-00053.

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Naz, Najia, Abdul Haseeb Malik, Abu Bakar Khurshid, Furqan Aziz, Bader Alouffi, M. Irfan Uddin y Ahmed AlGhamdi. "Efficient Processing of Image Processing Applications on CPU/GPU". Mathematical Problems in Engineering 2020 (10 de octubre de 2020): 1–14. http://dx.doi.org/10.1155/2020/4839876.

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Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.

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Rahimi, Masoud, Esmaeil Asadi Khameneh, Hamid Riazi-Esfahani, Tahereh Mahmoudi, Elias Khalili Pour y Rahele Kafieh. "Application of ImageJ in Optical Coherence Tomography Angiography (OCT-A): A Literature Review". Journal of Ophthalmology 2023 (22 de noviembre de 2023): 1–11. http://dx.doi.org/10.1155/2023/9479183.

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Background. This study aimed to review the literature on the application of ImageJ in optical coherence tomography angiography (OCT-A) images. Methods. A general search was performed in PubMed, Google Scholar, and Scopus databases. The authors evaluated each of the selected articles in order to assess the implementation of ImageJ in OCT-A images. Results. ImageJ can aid in reducing artifacts, enhancing image quality to increase the accuracy of the process and analysis, processing and analyzing images, generating comparable parameters such as the parameters that assess perfusion of the layers (vessel density (VD), skeletonized density (SD), and vessel length density (VLD)) and the parameters that evaluate the structure of the layers (fractal dimension (FD), vessel density index (VDI), and lacunarity (LAC)), and the foveal avascular zone (FAZ) that are used widely in the retinal and choroidal studies), and establishing diagnostic criteria. It can help to save time when the dataset is huge with numerous plugins and options for image processing and analysis with reliable results. Diverse studies implemented distinct binarization and thresholding techniques, resulting in disparate outcomes and incomparable parameters. Uniformity in methodology is required to acquire comparable data from studies employing diverse processing and analysis techniques that yield varied outcomes. Conclusion. Researchers and professionals might benefit from using ImageJ because of how quickly and correctly it processes and analyzes images. It is highly adaptable and potent software, allowing users to evaluate images in a variety of ways. There exists a diverse range of methodologies for analyzing OCTA images through the utilization of ImageJ. However, it is imperative to establish a standardized strategy to ensure the reliability and consistency of the method for research purposes.

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Лун, Сюй, Xu Long, Йан Йихуа, Yan Yihua, Чэн Цзюнь y Cheng Jun. "Guided filtering for solar image/video processing". Solar-Terrestrial Physics 3, n.º 2 (9 de agosto de 2017): 9–15. http://dx.doi.org/10.12737/stp-3220172.

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A new image enhancement algorithm employing guided filtering is proposed in this work for enhancement of solar images and videos, so that users can easily figure out important fine structures imbedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results prove that the proposed algorithm gives significant enhancement of visual quality of solar images beyond original input and several classical image en-hancement algorithms, thus facilitating easier determi-nation of interesting solar burst activities from recorded images/movies.

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Robbins, Stuart J., E. Beau Bierhaus, Olivier Barnouin, Tod R. Lauer, John Spencer, Simone Marchi, Harold A. Weaver, Stefano Mottola, Hal Levison y Neil Dello Russo. "Imaging Lunar Craters with the Lucy Long Range Reconnaissance Imager (L’LORRI): A Resolution Test for NASA's Lucy Mission". Planetary Science Journal 4, n.º 12 (1 de diciembre de 2023): 234. http://dx.doi.org/10.3847/psj/ad01b4.

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Abstract NASA's Lucy mission is designed to better understand the unique population of Trojan asteroids. Trojans were probably captured in Jupiter's L4 and L5 points early in the solar system's evolution and little altered since then. A critical investigation of Lucy is to use its highest-resolution camera, the Lucy Long Range Reconnaissance Imager (L’LORRI), to image Trojans’ surfaces to understand their geology and impact crater populations. Through crater statistics, the population of smaller bodies that produced those impacts, relative age differences across the bodies, and other comparative investigations between bodies can be studied. Mapping the crater population to the minimum diameters needed to achieve Lucy's objectives might require image subsampling and deconvolution (“processing”) to improve the spatial resolution, a process whereby multiple, slightly offset images are merged to create a single, better-sampled image and deconvolved with L’LORRI's point-spread function. Lucy's first Earth Gravity Assist (EGA1) provided an opportunity to test this process's accuracy using L’LORRI images of the Moon, whose crater population is well characterized and therefore provides ground-truth testing. Specifically, the lunar crater imaging by L’LORRI during EGA1 allowed us to compare crater statistics derived from raw and processed L’LORRI images with ground-truth statistics derived from higher-resolution lunar imaging from other missions. The results indicate the processing can improve impact crater statistics such that features can be identified and measured to ≈70% the diameter that they can otherwise be reliably mapped on native L’LORRI images. This test's results will be used in the observation designs for the Lucy flyby targets.

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Chou, Szu-Yuan, Cindy Chan, Yu-Chieh Lee, Tzu-Ning Yu, Chii-Ruey Tzeng y Chi-Huang Chen. "Evaluation of adenomyosis after gonadotrophin-releasing hormone agonist therapy using ultrasound post-processing imaging: a pilot study". Journal of International Medical Research 48, n.º 6 (junio de 2020): 030006052092005. http://dx.doi.org/10.1177/0300060520920056.

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Objective We explored a method for the quantitative sonographic analysis of myometrial texture using computer-aided image analysis software to assess outcomes following treatment with gonadotrophin-releasing hormone (GnRH) agonist for adenomyosis in women with infertility. Method Data for patients with ultrasound images of the myometrium obtained at Taipei Medical University Hospital from 1 September 2018 to 5 April 5 2019 were analyzed. Only 10 patients with 20 ultrasound images matched the eligibility criteria. The images were divided into pre-treatment (n = 10) and post-treatment images (n = 10) and quantitative grayscale histograms were obtained from the ultrasound images using publicly available ImageJ computer-aided image analysis software. We analyzed the differences between the pre- and post-treatment images using the Mann–Whitney test and compared the results with outcomes assessed by serum CA-125 levels. Results Image analysis of the grayscale histograms revealed significant differences between before and after treatment. The classification of the myometrium pre-treatment and post-treatment was similar using CA-125 and histogram grayscale analysis. Conclusion Computer-aided image analysis of grayscale histograms of the myometrium obtained from ultrasound images is an alternative method for assessing myometrial conditions after GnRH agonist treatment in patients with adenomyosis.

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Abdelkader, Mohamed. "MATLAB Algorithms for Diameter Measurements of Textile Yarns and Fibers through Image Processing Techniques". Materials 15, n.º 4 (10 de febrero de 2022): 1299. http://dx.doi.org/10.3390/ma15041299.

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Textile yarns are the fundamental building blocks in the fabric industry. The measurement of the diameter of the yarn textile and fibers is crucial in textile engineering as the diameter size and distribution can affect the yarn’s properties, and image processing can provide automatic techniques for faster and more accurate determination of the diameters. In this paper, facile and new methods to measure the yarn’s diameter and its individual fibers diameter based on image processing algorithms that can be applied to microscopic digital images. Image preprocessing such as binarization and morphological operations on the yarn image were used to measure the diameter automatically and accurately compared to the manual measuring using ImageJ software. In addition to the image preprocessing, the circular Hough transform was used to measure the diameter of the individual fibers in a yarn’s cross-section and count the number of fibers. The algorithms were built and deployed in a MATLAB (R2020b, The MathWorks, Inc., Natick, Massachusetts, United States) environment. The proposed methods showed a reliable, fast, and accurate measurement compared to other different image measuring softwares, such as ImageJ.

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Liu Jiansi, 刘建思, 尹丽菊 Yin Liju, 潘金凤 Pan Jinfeng, 崔玉敏 Cui Yumin y 汤祥裕 Tang Xiangyu. "基于参数化对数图像处理模型的光照不均匀图像的边缘检测算法". Laser & Optoelectronics Progress 58, n.º 22 (2021): 2210005. http://dx.doi.org/10.3788/lop202158.2210005.

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Priyadharsini, M. Suriya y J. G. R. Sathiaseelan. "The New Robust Adaptive Median Filter for Denoising Cancer Images Using Image Processing Techniques". Indian Journal Of Science And Technology 16, n.º 35 (26 de septiembre de 2023): 2813–21. http://dx.doi.org/10.17485/ijst/v16i35.1024.

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Margaj, Sampada. "Image Pre-Processing of USG Images by Adjusting Varying Luminance Trend and Applying Morphological Operations on Segmented Image using Thresholding". International Journal of Science and Research (IJSR) 12, n.º 2 (5 de febrero de 2023): 1080–84. http://dx.doi.org/10.21275/sr23219135729.

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Destyningtias, Budiani, Andi Kurniawan Nugroho y Sri Heranurweni. "Analisa Citra Medis Pada Pasien Stroke dengan Metoda Peregangan Kontras Berbasis ImageJ". eLEKTRIKA 10, n.º 1 (19 de junio de 2019): 15. http://dx.doi.org/10.26623/elektrika.v10i1.1105.

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This study aims to develop medical image processing technology, especially medical images of CT scans of stroke patients. Doctors in determining the severity of stroke patients usually use medical images of CT scans and have difficulty interpreting the extent of bleeding. Solutions are used with contrast stretching which will distinguish cell tissue, skull bone and type of bleeding. This study uses contrast stretching from the results of CT Scan images produced by first turning the DICOM Image into a JPEG image using the help of the ImageJ program. The results showed that the histogram equalization method and statistical texture analysis could be used to distinguish normal MRI and abnormal MRI detected by stroke.Keywords : Stroke, MRI, Dicom, JPEG, ImageJ, Contrast Stretching

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32

Pereyra Irujo, Gustavo. "IRimage: open source software for processing images from infrared thermal cameras". PeerJ Computer Science 8 (10 de mayo de 2022): e977. http://dx.doi.org/10.7717/peerj-cs.977.

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IRimage aims at increasing throughput, accuracy and reproducibility of results obtained from thermal images, especially those produced with affordable, consumer-oriented cameras. IRimage processes thermal images, extracting raw data and calculating temperature values with an open and fully documented algorithm, making this data available for further processing using image analysis software. It also allows the making of reproducible measurements of the temperature of objects in a series of images, and produce visual outputs (images and videos) suitable for scientific reporting. IRimage is implemented in a scripting language of the scientific image analysis software ImageJ, allowing its use through a graphical user interface and also allowing for an easy modification or expansion of its functionality. IRimage’s results were consistent with those of standard software for 15 camera models of the most widely used brand. An example use case is also presented, in which IRimage was used to efficiently process hundreds of thermal images to reveal subtle differences in the daily pattern of leaf temperature of plants subjected to different soil water contents. IRimage’s functionalities make it better suited for research purposes than many currently available alternatives, and could contribute to making affordable consumer-grade thermal cameras useful for reproducible research.

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Abdulhamid, Mohanad y Lwanga Wanjira. "Image Processing Techniques Based Crowd Size Estimation". Radioelectronics. Nanosystems. Information Technologies 12, n.º 3 (30 de octubre de 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 of different methods as advantageous features as well as negative characteristics of each technique is delineated. The main objective of this paper is to use image processing techniques to estimate the size of a crowd from a still photograph. The simulation results show that the different images have different efficiencies.

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Nandan, Anamika y Dr Satya Ranjan Pattanaik. "Brain Tumor Detection using Image Processing". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 07 (22 de julio de 2024): 1–13. http://dx.doi.org/10.55041/ijsrem36565.

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It is very difficult for doctors to detect a brain tumor at an early stage. MRI images are more susceptible to noise and other environmental disturbances. Therefore, it becomes difficult for doctors to determine the tumor and its causes. So, we came up with a system in which the system will detect a brain tumor from images. Here we are converting an image to a grayscale image. We apply filters to the image to remove noise and other environmental clutter from the image. The system will process the selected image using preprocessing steps. At the same time, different algorithms are used to detect the tumor from the image. But the edges of the image will not be sharp in the early stages of a brain tumor. So here we are applying image segmentation to the image to detect the edges of the images. We have proposed an image segmentation process and a variety of image filtering techniques to obtain image characteristics. Through this entire process, accuracy can be improved. This system is implemented in the Matlab. Keywords : Brain Tumor, classification, Segmentation.

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Kornus, W., A. Magariños, M. Pla, E. Soler y F. Perez. "PHOTOGRAMMETRIC PROCESSING USING ZY-3 SATELLITE IMAGERY". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (10 de marzo de 2015): 109–13. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-109-2015.

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This paper evaluates the stereoscopic capacities of the Chinese sensor ZiYuan-3 (ZY-3) for the generation of photogrammetric products. The satellite was launched on January 9, 2012 and carries three high-resolution panchromatic cameras viewing in forward (22º), nadir (0º) and backward direction (-22º) and an infrared multi-spectral scanner (IRMSS), which is slightly looking forward (6º). The ground sampling distance (GSD) is 2.1m for the nadir image, 3.5m for the two oblique stereo images and 5.8m for the multispectral image. The evaluated ZY-3 imagery consists of a full set of threefold-stereo and a multi-spectral image covering an area of ca. 50km x 50km north-west of Barcelona, Spain. The complete photogrammetric processing chain was executed including image orientation, the generation of a digital surface model (DSM), radiometric image correction, pansharpening, orthoimage generation and digital stereo plotting. All 4 images are oriented by estimating affine transformation parameters between observed and nominal RPC (rational polynomial coefficients) image positions of 17 ground control points (GCP) and a subsequent calculation of refined RPC. From 10 independent check points RMS errors of 2.2m, 2.0m and 2.7m in X, Y and H are obtained. Subsequently, a DSM of 5m grid spacing is generated fully automatically. A comparison with the Lidar data results in an overall DSM accuracy of approximately 3m. In moderate and flat terrain higher accuracies in the order of 2.5m and better are achieved. In a next step orthoimages from the high resolution nadir image and the multispectral image are generated using the refined RPC geometry and the DSM. After radiometric corrections a fused high resolution colour orthoimage with 2.1m pixel size is created using an adaptive HSL method. The pansharpen process is performed after the individual geocorrection due to the different viewing angles between the two images. In a detailed analysis of the colour orthoimage artifacts are detected covering an area of 4691ha, corresponding to less than 2% of the imaged area. Most of the artifacts are caused by clouds (4614ha). A minor part (77ha) is affected by colour patch, stripping or blooming effects. For the final qualitative analysis on the usability of the ZY-3 imagery for stereo plotting purposes stereo combinations of the nadir and an oblique image are discarded, mainly due to the different pixel size, which produces difficulties in the stereoscopic vision and poor accuracy in positioning and measuring. With the two oblique images a level of detail equivalent to 1:25.000 scale is achieved for transport network, hydrography, vegetation and elements to model the terrain as break lines. For settlement, including buildings and other constructions a lower level of detail is achieved equivalent to 1:50.000 scale.

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Saxena, Khushboo y Yogesh Kumar Gupta. "Analysis of Image Processing Strategies Dedicated to Underwater Scenarios". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 3s (18 de marzo de 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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Louk, Andreas Christian, Gede Bayu Suparta y Nurul Hidayah. "Image Processing for Multiple Micro-Radiography Images". Advanced Materials Research 896 (febrero de 2014): 676–80. http://dx.doi.org/10.4028/www.scientific.net/amr.896.676.

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An image processing method has been developed for processing multiple images of x-ray micro-radiography. An x-ray micro-radiography image reflects quantum mottle so that its information content may tends to be corrupted. Therefore, a digital processing method has been developed to reduce the effect of quantum mottle as well as reducing the noise level. A set of radiographs are collected then summed. An image subtraction by a background image is carried out prior to the summation process. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) after processing are compared with the SNR and CNR prior to the processing. As a result the final image for small specimen under x-ray micro-radiography inspection is better than original image without processing based on SNR and CNR assessments.

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38

Bento, Tiago, Duarte Val´erio, Pedro Teodoro y Jorge Martins. "Fractional Order Image Processing of Medical Images". Journal of Applied Nonlinear Dynamics 6, n.º 2 (junio de 2017): 181–91. http://dx.doi.org/10.5890/jand.2017.06.005.

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Tehuacanero, S., A. Lopéz y J. Reyes-Gasga. "Image Processing of HRTEM images of Hydroxyapatite". Microscopy and Microanalysis 9, S02 (18 de julio de 2003): 1304–5. http://dx.doi.org/10.1017/s1431927603446527.

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Uchida, Seiichi. "Image processing and recognition for biological images". Development, Growth & Differentiation 55, n.º 4 (7 de abril de 2013): 523–49. http://dx.doi.org/10.1111/dgd.12054.

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41

HERLIN, I. L., I. COHEN y S. BOUZIDI. "Image Processing for Sequences of Oceanographic Images". Journal of Visualization and Computer Animation 7, n.º 3 (julio de 1996): 169–76. http://dx.doi.org/10.1002/(sici)1099-1778(199607)7:3<169::aid-vis150>3.0.co;2-h.

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42

Kammala, Sai Sravanthi. "Identifying glaucoma fundus images using image processing". International Journal of Communication and Information Technology 1, n.º 2 (1 de julio de 2020): 33–36. http://dx.doi.org/10.33545/2707661x.2020.v1.i2a.17.

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43

Hoshi, Sujin, Kuniharu Tasaki, Kazushi Maruo, Yuta Ueno, Haruhiro Mori, Shohei Morikawa, Yuki Moriya, Shoko Takahashi, Takahiro Hiraoka y Tetsuro Oshika. "Improvement in Dacryoendoscopic Visibility after Image Processing Using Comb-Removal and Image-Sharpening Algorithms". Journal of Clinical Medicine 11, n.º 8 (7 de abril de 2022): 2073. http://dx.doi.org/10.3390/jcm11082073.

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Recently, a minimally invasive treatment for lacrimal passage diseases was developed using dacryoendoscopy. Good visibility of the lacrimal passage is important for examination and treatment. This study aimed to investigate whether image processing can improve the dacryoendoscopic visibility using comb-removal and image-sharpening algorithms. We processed 20 dacryoendoscopic images (original images) using comb-removal and image-sharpening algorithms. Overall, 40 images (20 original and 20 post-processing) were randomly presented to the evaluators, who scored each image on a 10-point scale. The scores of the original and post-processing images were compared statistically. Additionally, in vitro experiments were performed using a test chart to examine whether image processing could improve the dacryoendoscopic visibility in a turbid fluid. The visual score (estimate ± standard error) of the images significantly improved from 3.52 ± 0.26 (original images) to 5.77 ± 0.28 (post-processing images; p < 0.001, linear mixed-effects model). The in vitro experiments revealed that the contrast and resolution of images in the turbid fluid improved after image processing. Image processing with our comb-removal and image-sharpening algorithms improved dacryoendoscopic visibility. The techniques used in this study are applicable for real-time processing and can be easily introduced in clinical practice.

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Premalatha, Mrs M., A. Heymath Kumar, M. Manoj Kumar, P. Pavithran y K. Shatyadeep. "Drugged Eye Detection Using Image Processing". International Journal for Research in Applied Science and Engineering Technology 11, n.º 4 (30 de abril de 2023): 1577–82. http://dx.doi.org/10.22214/ijraset.2023.50427.

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Abstract: Drugs are a major problem in economic and many losses in worldwide. In this project, an image processing approach is proposed for identifying drugged eye based on convolutional neural network. According to the CNN algorithm, eye image details are taken by the existing packages from the front end used in this project. However, it can take a few moments. So, this proposed system can be used to identify drugged eyes quickly and automatically. The eye images dataset are taken from Kaggle. These images are taken as a training set for this drugged eye detection. This proposed approach is composed of the following main steps that getting input image, Image Preprocessing, identifying reddish places, highlight those affected places, Verifying training set, showing result. Few types of eyes like drugged socially may missed to identify. This approach was tested according to drugged eye type and its' stages, such as drug consumed and not consumed. The algorithm was used for detecting the white area of eye present in given input image. Images were provided for training, such as drugged eye images and normal eye images. Before the image processing, images were converted to color models, because of find out the most suitable color model for this approach. Local Binary Pattern was used for feature extraction and Support erosion method was used for creating the model. According to this approach, drugged eyes can be identified in the average accuracy of 95%.

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45

Campello de Magalhães, Roger, Silvia Cristina Martini Rodrigues, Márcia Aparecida Bissaco da Silva, Luiz Teruo Kawamoto y Alessandro Pereira da Silva. "Free Software for the Detection of Structures in Breast Images". Applied Mechanics and Materials 590 (junio de 2014): 814–18. http://dx.doi.org/10.4028/www.scientific.net/amm.590.814.

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The use of mammography as a method for the early detection of breast cancer reduces mortality from it. To aid in the diagnosis there are several free image processing software with the purpose of extracting characteristics such as microcalcification, nodules or edges of important structures. The objective of this study is to indicate which filters are best to aid in the processing of the images in three of these free software. The free software ImageJ, ImageTool and Mipav were used for the processing of 60 mammographic images from the Mini-MIAS database focusing the mean, median and Gaussian filters. The Haralick descriptors (second angular moment, contrast, entropy and correlation) and difference of values of gray levels (standard deviation, mean, minimum and maximum) were extracted from the resulting images. Then the results were compared taking into consideration the purpose of the processing. It was observed that it's best to use the mean and median filters from the Mipav software if the purpose of the processing is to leave the images with higher contrast levels. However, if the purpose is to obtain higher entropy levels, the ImageTool software should be used. It was also observed a similar processing time among the three software. The filter choice will depend on the type of noise to be removed from the image. For "Salt and Pepper" noise the mean filter should be used, while for the impulsive noise, the median one should be used. The results allowed the conclusion that the choice of the software to perform the processing of the mammographic images depends on the purpose of the processing application, if it's to increase the contrast in the image or if it's to extract other characteristics of diagnostic interest.

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Pouchin, Pierre, Rayan Zoghlami, Rémi Valarcher, Maxence Delannoy, Manon Carvalho, Clémence Belle, Marc Mongy, Sophie Desset y Frédéric Brau. "Easing batch image processing from OMERO: a new toolbox for ImageJ". F1000Research 11 (12 de septiembre de 2022): 392. http://dx.doi.org/10.12688/f1000research.110385.2.

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The Open Microscopy Environment Remote Objects (OMERO) is an open-source image manager used by many biologists to store, organize, view, and share microscopy images, while the open-source software ImageJ/Fiji is a very popular program used to analyse them. However, there is a lack of an easy-to-use generic tool to run a workflow on a batch of images without having to download them to local computers, and to automatically organize the results in OMERO. To offer this functionality, we have built (i) a library in Java: “Simple OMERO Client”, to communicate with an OMERO database from Java software, (ii) an ImageJ/Fiji plugin to run a macro-program on a batch of images from OMERO and (iii) a new set of Macro Functions, “OMERO Macro extensions“, dedicated to interact with OMERO in macro-programming. The latter is intended for developers, with additional possibilities using tag criteria, while the “Batch OMERO plugin” is more geared towards non-IT scientists and has a very easy to use interface. Each tool is illustrated with a use case.

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47

Pouchin, Pierre, Rayan Zoghlami, Rémi Valarcher, Maxence Delannoy, Manon Carvalho, Clémence Belle, Marc Mongy, Sophie Desset y Frédéric Brau. "Easing batch image processing from OMERO: a new toolbox for ImageJ". F1000Research 11 (5 de abril de 2022): 392. http://dx.doi.org/10.12688/f1000research.110385.1.

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The Open Microscopy Environment Remote Objects (OMERO) is an open source image manager used by many biologists to store, organize, view and share microscopy images, while the open source software ImageJ/Fiji is a very popular program used to analyse them. However, there is a lack of an easy-to-use generic tool to run a workflow on a batch of images without having to download them to local computers; and to automatically organize the results in OMERO. To offer this functionality, we have built three tools in Java language: “Simple OMERO Client”, a library to communicate with an OMERO database from Java softwares ; an ImageJ/Fiji plugin to run a macro-program on a batch of images from OMERO and “OMERO Macro extensions“, a dedicated vocabulary to interact with OMERO in macro-programming. The latter is intended for developers, with additional possibilities using tag criteria, while the “Batch OMERO plugin” is more geared towards non-IT scientists and has a very easy to use interface. Both tools are illustrated with a use case.

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48

Wang, Xiaofeng, Yaduan Ruan y Xinggan Zhang. "Accuracy Improvement of High-Resolution Wide-Swath Spaceborne Synthetic Aperture Radar Imaging with Low Pule Repetition Frequency". Remote Sensing 15, n.º 15 (31 de julio de 2023): 3811. http://dx.doi.org/10.3390/rs15153811.

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For a single-channel spaceborne synthetic aperture radar (SAR), the usage of a low pulse repetition frequency (PRF) is an effective technical way to extend the range swath. The sub-aperture imaging strategy is usually used to solve the problem of azimuth spectrum aliasing under the condition of a low PRF. However, the required up-sampling processing before the coherent synthesis of sub-images will lead to spectrum discontinuity between adjacent sub-images, which leads to an obvious grating lobe phenomenon after the process of sub-image synthesis, resulting in a significant decrease in image quality. For this issue, a high-resolution wide-swath (HRWS) imaging algorithm for a spaceborne SAR with a low PRF is proposed in this paper based on optimal spectrum shift processing. First, each sub-aperture is imaged using the typical range migration algorithm (RMA), and then all sub-images are up-sampled at the same time. Then, based on the criterion of the minimum grating lobe, the optimal spectrum shift is estimated. Finally, the spectrum of all sub-images is shifted and then all the shifted sub-images are synthesized coherently. The simulation data processing results verify the effectiveness of the proposed method.

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49

Ottapura, Sayooj, Rahul Mistry, Jatin Keni y Chaitanya Jage. "Underwater Image Processing using Graphics Processing Unit (GPU)". ITM Web of Conferences 32 (2020): 03041. http://dx.doi.org/10.1051/itmconf/20203203041.

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Image processing is a method used for enhancement of an image or to extract some useful information from the image. It is a type of signal processing in which input is an image and output may be an image or any characteristics/features associated with that image. In this paper we will be focusing on a specific type of Image Processing i.e. Underwater Image Processing. Underwater Image Processing has always faced the problem of imbalance in colour distribution and this problem can be tackled by the simplest algorithm for colour balancing. We will be proceeding with the assumption that the highest values of R, G, B observed in the image corresponds to white and the lowest values corresponds to darkness. The underwater images are majorly saturated by blue colour because of its short wavelength and in this paper, we aim to enhance the image. We proposed a colour balancing algorithm for normalizing the image. The entire process will first be carried out on a CPU followed by a GPU. We will then compare the speedup obtained. Speedup is an important parameter in the field on image processing since a better speedup can help reduce the computation time significantly while maintaining a higher efficiency.

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50

LIN, YUE-DER, HEN-WEI TSAO y FOK-CHING CHONG. "AN IMAGE PROCESSING ARCHITECTURE TO ENHANCE IMAGE CONTRAST". Biomedical Engineering: Applications, Basis and Communications 14, n.º 05 (25 de octubre de 2002): 215–17. http://dx.doi.org/10.4015/s1016237202000310.

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To have a good image contrast is an important issue in medical images. This paper introduces a feedback-type image processing architecture that can enhance image contrast without further digital image processing technique, e.g. histogram equalization. Compared with the conventional open-loop imaging system, the images derived by the proposed method has a full-range histogram without causing image distortion, and this is difficult to attain for open-loop imaging system.

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