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Journal articles on the topic 'Large image processing'

Author: Grafiati

Published: 25 May 2024

Last updated: 19 July 2025

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S.M., Karpagavalli. "Large Scale Image Processing Using Hadoop Image Processing Interface System." Journal of Advances in Computational Intelligence Theory 5, no. 3 (2023): 28–36. https://doi.org/10.5281/zenodo.8317788.

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<em>Because of the expanding prevalence of modest advanced photography gear, individualized computing gadgets with simple to utilize cameras, and a general improvement of picture catch innovation concerning quality; the measure of information created by individuals every day demonstrates patterns of becoming quicker than the handling capacities of single gadgets. It turns out to be computationally wasteful to examine such tremendous information. The sum of raw information accessible has been expanding at an exponential rate. For the viable treatment of such monstrous information, the utilization of MapReduce system has been broadly came into core interest. In the course of the most recent couple of years, MapReduce has developed as the most famous processing worldview for parallel, cluster style and examination of expansive measure of information. In this paper, we are getting down to business around MapReduce, its favorable circumstances, drawbacks and how it tends to be utilized in mix with other innovation.</em>

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Lee, Youngrim, Wanyong Park, Hyunchun Park, and Daesik Shin. "FAST Design for Large-Scale Satellite Image Processing." Journal of the Korea Institute of Military Science and Technology 25, no. 4 (2022): 372–80. http://dx.doi.org/10.9766/kimst.2022.25.4.372.

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This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.

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Tripathi, Rakesh, and Neelesh Gupta. "A Review on Segmentation Techniques in Large-Scale Remote Sensing Images." SMART MOVES JOURNAL IJOSCIENCE 4, no. 4 (2018): 7. http://dx.doi.org/10.24113/ijoscience.v4i4.143.

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Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.

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Vinichuk, O. N., and V. I. Dravitsa. "Development of Algorithms for Processing Images of Large Volumes." Digital Transformation 28, no. 2 (2022): 52–60. http://dx.doi.org/10.35596/2522-9613-2022-28-2-52-60.

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In recent years, interest in digital image processing has increased significantly, so it is no coincidence that digital processing is one of the intensively developed areas of research. When working with a computer system, a rather important factor is the high-quality display of images, as a result of which the methods of processing and improving images are no less important factors, which are not only responsible for the highquality display of the image, but also allow to increase the visibility of interesting details in the image. Today it is quite difficult to find an application or a web application with a simple and user-friendly interface, as well as with relatively low characteristics in terms of energy consumption needed to supply the operating system and the device in general. This article presents new algorithms that improve the efficiency of image processing by reducing application loading and processing time, as well as by reducing the load on the operating system.

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Pal, N. R., and J. C. Bezdek. "Complexity reduction for "large image" processing." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, no. 5 (2002): 598–611. http://dx.doi.org/10.1109/tsmcb.2002.1033179.

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Khellah, F., P. Fieguth, M. J. Murray, and M. Allen. "Statistical processing of large image sequences." IEEE Transactions on Image Processing 14, no. 1 (2005): 80–93. http://dx.doi.org/10.1109/tip.2004.838703.

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Utkarsh, Gupta, Kumar Sudhanshu, Singhal Devansh, Tomar Parth, and Kumar Ajay. "IMAGE PROCESSING SYSTEM USING JAVA." International Journal of Innovative Research in Information Security VII, no. IV (2020): 36–40. https://doi.org/10.26562/ijiris.2020.v0704.002.

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The article is all about the Image Processing System that can be defined as, processing and altering an existing image in the desired manner. Image is one of the perceptible sources in applications of Image Processing including a large number of tools and techniques which help to extract complex features of an image. Probably the most powerful image processing system is the human brain together with the eye. The system receives, enhances, and stores images at enormous rates of speed. The objective of Image Processing is to visually enhance or statistically evaluate some aspect of an image not readily apparent in its original form. Several technologies playing on images in real-time but image processing is the real core. This paper discusses the overview of development; implementation of operations required for quality image production and also discusses image processing applications, tools, and techniques.

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Filatov, Valerii, Anna Filatova, Anatolii Povoroznyuk, and Shakhin Omarov. "IMAGE CLASSIFIER FOR FAST SEARCH IN LARGE DATABASES." Advanced Information Systems 8, no. 2 (2024): 12–19. http://dx.doi.org/10.20998/2522-9052.2024.2.02.

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Relevance. The avalanche-like growth in the amount of information on the Internet necessitates the development of effective methods for quickly processing such information in information systems. Clustering of news information is carried out by taking into account both the morphological analysis of texts and graphic content. Thus, an urgent task is the clustering of images accompanying textual information on various web resources, including news portals. The subject of study is an image classifier that exhibits low sensitivity to increased information in databases. The purpose of the article is to enhance the efficiency of searching for identical images in databases experiencing a daily influx of 10-12 thousand images, by developing an image classifier. Methods used: mathematical modeling, content-based image retrieval, two-dimensional discrete cosine transform, image processing methods, decision-making methods. The following results were obtained. An image classifier has been developed with low sensitivity to increased database information. The properties of the developed classifier have been analyzed. The experiments demonstrated that clustering information based on images using the developed classifier proved to be sufficiently fast and cost-effective in terms of information volumes and computational power requirements.

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P, Sai Gyaneshwar Chary, Rahul Pasha Shaik, Sandeep Sirra, and Vishwa Shanthi M. "TEXT TO IMAGE GENERATION IN PYTHON USING IMAGEN MODEL AND STREAMLIT." TEXT TO IMAGE GENERATION IN PYTHON USING IMAGEN MODEL AND STREAMLIT 2 2, M. Vishwa Shanthi (2023): 54. https://doi.org/10.5281/zenodo.7868137.

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Text-to-image generation is a fascinating application of computer vision and natural language processing, where the goal is to generate realistic and diverse images based on textual descriptions. In this project, we propose a text-to-image generation system using Python programming language and two main libraries, Imagen and Streamlit. The system consists of a generative adversarial network (GAN) model trained on a large dataset of images and their corresponding captions, and a text processing and generation module. 

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Liu, Zhi-Qiang. "Bayesian Paradigms in Image Processing." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 01 (1997): 3–33. http://dx.doi.org/10.1142/s0218001497000020.

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A large number of image and spatial information processing problems involves the estimation of the intrinsic image information from observed images, for instance, image restoration, image registration, image partition, depth estimation, shape reconstruction and motion estimation. These are inverse problems and generally ill-posed. Such estimation problems can be readily formulated by Bayesian models which infer the desired image information from the measured data. Bayesian paradigms have played a very important role in spatial data analysis for over three decades and have found many successful applications. In this paper, we discuss several aspects of Bayesian paradigms: uncertainty present in the observed image, prior distribution modeling, Bayesian-based estimation techniques in image processing, particularly, the maximum a posteriori estimator and the Kalman filtering theory, robustness, and Markov random fields and applications.

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Shinde, Prof Dyanda. "Air Pollution Checker Using Image Processing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26515.

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In this paper we present a new method to visualize air pollutant through image processing. In order to achieve a realistic effect, we further enhance thus above obtained images in spatial domain. In the proposed method we map the densities of air pollutants to different gray levels, and visualize them by blending those gray levels with background images. The proposed method can visualize large-scale air pollution data from different viewpoints in real time and provide the resulting image with any resolution theoretically, which is very important and favorable for the Internet transmission. Keywords: Machine Learning; Air Pollution; Air Pollution Prediction,images

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Dong, Lei, Tingtao Zhang, Fangjian Liu, Rui Liu, and Hongjian You. "GPU Acceleration for SAR Satellite Image Ortho-Rectification." Remote Sensing 16, no. 7 (2024): 1301. http://dx.doi.org/10.3390/rs16071301.

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Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric positioning models for SAR images. In order to address the high computation time of pixel-by-pixel ortho-rectification in SAR satellite images, a GPU-accelerated pixel-by-pixel correction method based on a rational polynomial coefficients (RPCs) model is proposed, which improves the efficiency of SAR satellite image ortho-rectification. Furthermore, in order to solve the problem of linearly increasing processing time for the ortho-rectification of multiple SAR images in large overlapping areas, a multi-GPU collaborative acceleration strategy for the ortho-rectification of multiple SAR images in large overlapping areas is proposed, achieving efficient ortho-rectification processing of multiple SAR image data in large overlapping areas. By conducting ortho-rectification experiments on 20 high-resolution SAR images from the Gaofen-3 satellite, the feasibility and efficiency of the multi-GPU collaborative acceleration processing algorithm are verified.

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Kim, Yoon-Ki, and Yongsung Kim. "DiPLIP: Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference." Electronics 9, no. 10 (2020): 1664. http://dx.doi.org/10.3390/electronics9101664.

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Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the distributed parallel processing system, the orchestration of technology has become crucial for proper management of computing resources, in terms of allocating computing resources, setting up a programming environment, and deploying user applications. In this paper, we present a new distributed parallel processing platform for real-time large-scale image processing based on deep learning model inference, called DiPLIP. It provides a scheme for large-scale real-time image inference using buffer layer and a scalable parallel processing environment according to the size of the stream image. It allows users to easily process trained deep learning models for processing real-time images in a distributed parallel processing environment at high speeds, through the distribution of the virtual machine container.

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Remondino, F., E. Nocerino, I. Toschi, and F. Menna. "A CRITICAL REVIEW OF AUTOMATED PHOTOGRAMMETRIC PROCESSING OF LARGE DATASETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W5 (August 21, 2017): 591–99. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w5-591-2017.

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The paper reports some comparisons between commercial software able to automatically process image datasets for 3D reconstruction purposes. The main aspects investigated in the work are the capability to correctly orient large sets of image of complex environments, the metric quality of the results, replicability and redundancy. Different datasets are employed, each one featuring a diverse number of images, GSDs at cm and mm resolutions, and ground truth information to perform statistical analyses of the 3D results. A summary of (photogrammetric) terms is also provided, in order to provide rigorous terms of reference for comparisons and critical analyses.

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Piat, Sebastien, Nairi Usher, Simone Severini, Mark Herbster, Tommaso Mansi, and Peter Mountney. "Image classification with quantum pre-training and auto-encoders." International Journal of Quantum Information 16, no. 08 (2018): 1840009. http://dx.doi.org/10.1142/s0219749918400099.

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Computer vision has a wide range of applications from medical image analysis to robotics. Over the past few years, the field has been transformed by machine learning and stands to benefit from potential advances in quantum computing. The main challenge for processing images on current and near-term quantum devices is the size of the data such devices can process. Images can be large, multidimensional and have multiple color channels. Current machine learning approaches to computer vision that exploit quantum resources require a significant amount of manual pre-processing of the images in order to be able to fit them onto the device. This paper proposes a framework to address the problem of processing large scale data on small quantum devices. This framework does not require any dataset-specific processing or information and works on large, grayscale and RGB images. Furthermore, it is capable of scaling to larger quantum hardware architectures as they become available. In the proposed approach, a classical autoencoder is trained to compress the image data to a size that can be loaded onto a quantum device. Then, a Restricted Boltzmann Machine (RBM) is trained on the D-Wave device using the compressed data, and the weights from the RBM are then used to initialize a neural network for image classification. Results are demonstrated on two MNIST datasets and two medical imaging datasets.

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Liu, Hanwen, Jordon Li, Qipei Tan, et al. "Review on Image Processing Method based on AI Large Models." Scientific Journal of Intelligent Systems Research 7, no. 2 (2025): 57–64. https://doi.org/10.54691/mh0tqs13.

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The application of AI large models in image processing technology is continuously expanding and deepening. They automatically extract feature information from raw image data through deep learning technology and perform efficient analysis and processing. This article provides a review of the current state of image processing technology, focusing on the analysis of image processing techniques based on machine learning and AI large models. It is found that the introduction of AI large models has led to more rapid and intelligent development of image processing technology.

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Grigoryan, Artyom, Alexis Gomez, Sos Agaian, and Karen Panetta. "Quantum Edge Detection and Convolution Using Paired Transform-Based Image Representation." Information 16, no. 4 (2025): 255. https://doi.org/10.3390/info16040255.

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Classical edge detection algorithms often struggle to process large, high-resolution image datasets efficiently. Quantum image processing offers a promising alternative, but current implementations face significant challenges, such as time-consuming data acquisition, complex device requirements, and limited real-time processing capabilities. This work presents a novel paired transform-based quantum representation for efficient image processing. This representation enables the parallelization of convolution operations, simplifies gradient calculations, and facilitates the processing of one-dimensional and two-dimensional signals. We demonstrate that our approach achieves improved processing speed compared to classical methods while maintaining comparable accuracy. The successful implementation of real-world images highlights the potential of this research for large-scale quantum image processing, architecture-specific optimizations, and applications beyond edge detection.

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Wang, Chaoli, John P. Reese, Huan Zhang, et al. "Similarity-based visualization of large image collections." Information Visualization 14, no. 3 (2013): 183–203. http://dx.doi.org/10.1177/1473871613498519.

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Effective techniques for organizing and visualizing large image collections are in growing demand as visual search gets increasingly popular. Targeting an online astronomy archive with thousands of images, we present our solution for image search and clustering based on the evaluation of image similarity using both visual and textual information. Time-consuming image similarity computation is accelerated using graphics processing unit. To lay out images, we introduce iMap, a treemap-based representation for visualizing and navigating image search and clustering results. iMap not only makes effective use of available display area to arrange images but also maintains stable update when images are inserted or removed during the query. We also develop an embedded visualization that integrates image tags for in-place search refinement. To show the effectiveness of our approach, we demonstrate experimental results, compare our iMap layout with a force-directed layout, and conduct a comparative user study. As a potential tool for astronomy education and outreach, we deploy our iMap to a large tiled display of nearly 50 million pixels.

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Sivakumar, R. D. Assistant Professor Department of Computer Science. "MODERNIZATION OF A PROGRESSIVEBTC ALGORITHM FOR DISTRIBUTED IMAGE COMPRESSION." Indian Journal of Recent Development Systems for Digitization 1, no. 3 (2024): 25–38. https://doi.org/10.5281/zenodo.10796956.

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The article discusses how the BTC (Block Truncation Coding) technique was used to construct a progressive algorithm for distributed image compression. Using several processing nodes in a distributed system, the suggested approach enables the compression of large images without compromising image quality or increasing processing times. The image is divided into blocks using the BTC approach. The compressed blocks are subsequently transmitted and combined by the processing nodes to create the original image. Since the number of processing nodes can be changed without compromising the compression ratio or image quality, the algorithm is progressive. In comparison to existing methods, the experimental findings show that the suggested technique achieves higher compression ratios and reduced processing times. The suggested approach can be applied in many different contexts where large image data needs to be efficiently compressed, such as multimedia communication and storage.

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Dong, Lei, Niangang Jiao, Tingtao Zhang, Fangjian Liu, and Hongjian You. "GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images." Applied Sciences 14, no. 4 (2024): 1528. http://dx.doi.org/10.3390/app14041528.

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This paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, successful in computer vision and optical satellite image matching, faces challenges when applied to satellite SAR images due to the presence of speckle noise, leading to increased matching errors. The SAR–SIFT method is explored and analyzed in-depth, considering the unique characteristics of satellite SAR images. To enhance the efficiency of matching identical feature points in two satellite SAR images, the paper proposes a Graphics Processing Unit (GPU) mapping implementation based on the SAR–SIFT algorithm. The paper introduces a multi-GPU collaborative acceleration strategy for SAR image matching. This strategy addresses the challenge of matching feature points in the region and embedding multiple SAR images in large areas. The goal is to achieve efficient matching processing of multiple SAR images in extensive geographical regions. The proposed multi-GPU collaborative acceleration algorithm is validated through experiments involving feature point extraction and matching using 21 GF-3 SAR images. The results demonstrate the feasibility and efficiency of the algorithm in enhancing the processing speed of matching feature points in large-scale satellite SAR images. Overall, the paper contributes to the advancement of SAR image processing techniques, specifically in feature point extraction and matching in large-scale applications.

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Nagy, Marius, and Naya Nagy. "Image processing: why quantum?" Quantum Information and Computation 20, no. 7&8 (2020): 616–26. http://dx.doi.org/10.26421/qic20.7-8-6.

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Quantum Image Processing has exploded in recent years with dozens of papers trying to take advantage of quantum parallelism in order to offer a better alternative to how current computers are dealing with digital images. The vast majority of these papers define or make use of quantum representations based on very large superposition states spanning as many terms as there are pixels in the image they try to represent. While such a representation may apparently offer an advantage in terms of space (number of qubits used) and speed of processing (due to quantum parallelism), it also harbors a fundamental flaw: only one pixel can be recovered from the quantum representation of the entire image, and even that one is obtained non-deterministically through a measurement operation applied on the superposition state. We investigate in detail this measurement bottleneck problem by looking at the number of copies of the quantum representation that are necessary in order to recover various fractions of the original image. The results clearly show that any potential advantage a quantum representation might bring with respect to a classical one is paid for dearly with the huge amount of resources (space and time) required by a quantum approach to image processing.

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Liang, Zheng, and Jian An Yuan. "Image Processing Technology for Aerial Camera Manipulator." Applied Mechanics and Materials 644-650 (September 2014): 4072–75. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4072.

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CCD aerial camera is one of the important means of obtaining the image information on the ground, it is through the collection, archiving, and reading to achieved the images acquisition. As the very large amounts of data of the images, it takes a lot of time far more than analysis and processing when archiving and reading, so that not only difficult achieve real-time detection and processing, but also causing a waste of storage space. Therefore, the research of image compression and other processing technology has become important particularly.This paper use the wavelet coding to get images compression for the problem, and design the image processing system of aerial camera manipulator. This system designed by embedded modular, and ARINC 429 bus to achieve communications between the camera and the aircraft systems, make compression to the images which captured by the camera, and deal with the compressed image as stored, local zoom in and out, etc.

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Naresh, Babu Bynagari, and B. M. Asadullah A. "Artificial Intelligence for Image Processing and Analysis." Artificial Intelligence for Image Processing and Analysis 2021, no. 7 (2021): 11023–36. https://doi.org/10.5281/zenodo.5622684.

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A large number of persecutors have been interested in computer vision research. It evolves from the collection of raw data to the development of techniques and ideas that combine digital image processing, model recognition, machine learning, and computer graphic design in order to achieve a more complex result. The desire to work across a broad spectrum of disciplines and fields has drawn a large number of academics in recent years. With reference to recent technological and theoretical concepts, this paper provides an explanation of the development of computer vision, specifically in relation to the processing of images in various areas of their field application, as well as the future of computer vision. In order to find information in images and videos, such as information on events or descriptions, as well as picture patterns, students can use computer vision to analyze them. This was accomplished through the application of multi-purpose application domain methods in conjunction with large-scale data analysis. With its introduction to computer vision, image processing, and related studies, this paper makes a significant contribution to the most recent reviews in these fields by providing a broad overview of the field. To make things easier to understand, we divided the mainstream of computer vision into four categories: picture processing, object recognition, and machine learning, respectively (among others). Artificial intelligence (AI) has made significant strides in the fields of X-ray, ultrasound, computed tomography (CT), and other types of medical imaging in recent years. Many artificial intelligence-based tools are currently being developed to automate the analysis of medical images and to improve the accuracy of automatic image interpretation, both of which are important goals. Image analysis combined with the consistent face recognition factor enables readers to comprehend the effectiveness of the system and respond appropriately in situations where improvisation is required. Image analysis combined with the consistent face recognition factor with this study, the authors hope to provide readers with a comprehensive understanding of the problem while also opening up new avenues for further investigation.

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Gu, Yi, Chaoli Wang, Jun Ma, Robert J. Nemiroff, David L. Kao, and Denis Parra. "Visualization and recommendation of large image collections toward effective sensemaking." Information Visualization 16, no. 1 (2016): 21–47. http://dx.doi.org/10.1177/1473871616630778.

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In our daily lives, images are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image collections and their associated text information. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords but also supports detailed comparison for understanding and intuitive guidance for navigation. The visual exploration of iGraph is further enhanced with the implementation of bubble sets to highlight group memberships of nodes, suggestion of abnormal keywords or time periods based on text outlier detection, and comparison of four different recommendation solutions. For performance speedup, multiple graphics processing units and central processing units are utilized for processing and visualization in parallel. We experiment with two image collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.

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Mirwansyah, Dedy, and Arief Wibowo. "FRUIT IMAGE CLASSIFICATION USING DEEP LEARNING ALGORITHM: SYSTEMATIC LITERATURE REVIEW (SLR)." MULTICA SCIENCE AND TECHNOLOGY (MST) JOURNAL 2, no. 2 (2022): 120–23. http://dx.doi.org/10.47002/mst.v2i2.356.

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Systematic literature review (SLR) research studies various classification models with deep learning algorithms on fruit with digital images. In recent years, computer vision and processing techniques are increasingly useful in the fruit industry, especially for quality and color inspection, sizing, and shape sorting applications. Research in this area demonstrates the feasibility of using a machine computer vision system to improve product quality. Utilizing deep learning in the field of image processing or digital image processing, Image Processing is used to assist humans in recognizing and/or classifying objects quickly, and precisely, and can process large amounts of data simultaneously. Classifying fruit through a computerized system using deep learning algorithms with CNN, MASK-RCNN, FASTER-RCNN, and SSD models. Developed on the multilayer perceptron (MLP) layer, the algorithm is processed into two-dimensional data, to the image and is capable of classifying images with larger classes.

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Sun, Bing, Chuying Fang, Hailun Xu, and Anqi Gao. "A New Synthetic Aperture Radar (SAR) Imaging Method Combining Match Filter Imaging and Image Edge Enhancement." Sensors 18, no. 12 (2018): 4133. http://dx.doi.org/10.3390/s18124133.

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In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

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Long, Ma, Du Jiangbin, Zhao Jiayao, Wang Xuhao, and Peng Yangfan. "Large-field Astronomical Image Restoration and Superresolution Reconstruction using Deep Learning." Publications of the Astronomical Society of the Pacific 135, no. 1053 (2023): 114505. http://dx.doi.org/10.1088/1538-3873/ad0a04.

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Abstract The existing astronomical image restoration and superresolution reconstruction methods have problems such as low efficiency and poor results when dealing with images possessing large fields of view. Furthermore, these methods typically only handle fixed-size images and require step-by-step processing, which is inconvenient. In this paper, a neural network called Res&RecNet is proposed for the restoration and superresolution reconstruction of astronomical images with large fields of view for direct imaging instruments. This network performs feature extraction, feature correction, and progressive generation to achieve image restoration and superresolution reconstruction. The network is constructed using fully convolutional layers, allowing it to handle images of any size. The network can be trained using small samples and can perform image restoration and superresolution reconstruction in an end-to-end manner, resulting in high efficiency. Experimental results show that the network is highly effective in terms of processing astronomical images with complex scenes, generating image restoration results that improve the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) by 4.69 (dB)/0.073 and superresolution reconstruction results that improve the PSNR and SSIM by 1.97 (dB)/0.077 over those of the best existing algorithms, respectively.

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TAKAHASHI, Hiroshi, and Katsumi SANO. "Automatic Detection of Large Rocks by Image Processing." Shigen-to-Sozai 113, no. 3 (1997): 169–74. http://dx.doi.org/10.2473/shigentosozai.113.169.

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Zhao, Qianqian. "Image Processing of Large-Scale Pollution on Water." Journal of Physics: Conference Series 1486 (April 2020): 042019. http://dx.doi.org/10.1088/1742-6596/1486/4/042019.

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Alles, Guilherme Rezende, João L. D. Comba, Jean-Marc Vincent, Shin Nagai, and Lucas Mello Schnorr. "Measuring phenology uncertainty with large scale image processing." Ecological Informatics 59 (September 2020): 101109. http://dx.doi.org/10.1016/j.ecoinf.2020.101109.

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Seshamani, Sharmishtaa, Camilo Laiton, Gabor Kovacs, et al. "Cloud Pipelines for Large Scale Lightsheet Image Processing." Microscopy and Microanalysis 29, Supplement_1 (2023): 998. http://dx.doi.org/10.1093/micmic/ozad067.501.

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Mittleman, R. K., and N. W. Parker. "Micrograph image processing system." Proceedings, annual meeting, Electron Microscopy Society of America 44 (August 1986): 872–73. http://dx.doi.org/10.1017/s0424820100145704.

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We have developed a computer-driven image processing system for the analysis of STEM micrographs. Modern computer and data storage devices give the researcher the ability to record and store large numbers of images. Our system was designed to allow rapid viewing and to facilitate the measurement process. Making use of an IBM 4381-2 mainframe computer and a high resolution (1024 x 1280 pixels) Metheus Omega 500 display system, we have written a flexible, interactive system to display and process data from our electron microscopes. The system is menu-driven from a mouse and can call subroutines from fortran, pascal or APL.APL is an interpreted, interactive language that is particularly well suited to real time image processing. Algebraic manipulations can be performed with equal ease on arrays of any rank and the user can affect many complex operations on dataarrays, usually with a single line of code (e.g., gray scale expansion, zoom, inner and outer product, etc.).

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Liu, S., H. Li, X. Wang, L. Guo, and R. Wang. "STUDY ON MOSAIC AND UNIFORM COLOR METHOD OF SATELLITE IMAGE FUSION IN LARGE SREA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1099–102. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1099-2018.

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Due to the improvement of satellite radiometric resolution and the color difference for multi-temporal satellite remote sensing images and the large amount of satellite image data, how to complete the mosaic and uniform color process of satellite images is always an important problem in image processing. First of all using the bundle uniform color method and least squares mosaic method of GXL and the dodging function, the uniform transition of color and brightness can be realized in large area and multi-temporal satellite images. Secondly, using Color Mapping software to color mosaic images of 16bit to mosaic images of 8bit based on uniform color method with low resolution reference images. At last, qualitative and quantitative analytical methods are used respectively to analyse and evaluate satellite image after mosaic and uniformity coloring. The test reflects the correlation of mosaic images before and after coloring is higher than 95&amp;thinsp;% and image information entropy increases, texture features are enhanced which have been proved by calculation of quantitative indexes such as correlation coefficient and information entropy. Satellite image mosaic and color processing in large area has been well implemented.

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Mu, Ching-Yun, and Pin Kung. "Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions." Applied Sciences 14, no. 18 (2024): 8254. http://dx.doi.org/10.3390/app14188254.

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Image pre-processing is crucial for large fleet management. Many traffic videos are collected by closed-circuit television (CCTV), which has a fixed area monitoring for image analysis. This paper adopts the front camera installed in large vehicles to obtain moving traffic images, whereas CCTV is more limited. In practice, fleets often install cameras with different resolutions due to cost considerations. The cameras evaluate the front images with traffic lights. This paper proposes fuzzy enhancement with RGB and CIELAB conversions to handle multiple resolutions. This study provided image pre-processing adjustment comparisons, enabling further model training and analysis. This paper proposed fuzzy enhancement to deal with multiple resolutions. The fuzzy enhancement and fuzzy with brightness adjustment produced images with lower MSE and higher PSNR for the images of the front view. Fuzzy enhancement can also be used to enhance traffic light image adjustments. Moreover, this study employed You Only Look Once Version 9 (YOLOv9) for model training. YOLOv9 with fuzzy enhancement obtained better detection performance. This fuzzy enhancement made more flexible adjustments for pre-processing tasks and provided guidance for fleet managers to perform consistent image-enhancement adjustments for handling multiple resolutions.

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Zhang, Yang, Hangyu Xie, Shikai Zhuang, and Xiaoan Zhan. "Image Processing and Optimization Using Deep Learning-Based Generative Adversarial Networks (GANs)." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 5, no. 1 (2024): 50–62. http://dx.doi.org/10.60087/jaigs.v5i1.163.

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This paper introduces the application of generative adversarial networks (GANs) in image processing and optimization. GANs model can generate realistic images by co-training generator and discriminator, and achieve remarkable results in image restoration tasks. CATGAN and DCGAN are two commonly used GAN models applied to image classification and image restoration respectively. In addition, the global and local image patching methods can effectively fill the missing areas in the image and show good results in the restoration of large images. In conclusion, the image processing and optimization method based on GANs has shown great potential in practice and provides beneficial insight for future research and application in the field of image processing.

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Stoev, Stoicho. "Approaches in Using Python for Image Processing." Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series 12, no. 3 (2023): 122–28. http://dx.doi.org/10.56065/ijusv-ess/2023.12.3.122.

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Image processing is an increasingly widespread field in computer science. On the other hand, it is increasingly used both in modern business and in social communication. We will look at image manipulation methods, emphasizing approaches for comparing them. We will offer ways to process images included in popular specialized applications. We emphasize the use of the popular Python programming language, which is becoming more and more popular due to its ease of use and large range of tools. We will look at some of the image processing and analysis libraries used in Python.

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Bhatta, Janardan. "Large-scale image search with text for information retrieval." Journal of Innovations in Engineering Education 4, no. 1 (2021): 87–89. http://dx.doi.org/10.3126/jiee.v4i1.35390.

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Searching images in a large database is a major requirement in Information Retrieval Systems. Expecting image search results based on a text query is a challenging task. In this paper, we leverage the power of Computer Vision and Natural Language Processing in Distributed Machines to lower the latency of search results. Image pixel features are computed based on contrastive loss function for image search. Text features are computed based on the Attention Mechanism for text search. These features are aligned together preserving the information in each text and image feature. Previously, the approach was tested only in multilingual models. However, we have tested it in image-text dataset and it enabled us to search in any form of text or images with high accuracy.

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Sindhu, Sindhu, and V. Vaidhehi. "Classification of Human Organ Using Image Processing." Oriental journal of computer science and technology 10, no. 2 (2017): 333–37. http://dx.doi.org/10.13005/ojcst/10.02.11.

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The collection of large database of digital image has been used for efficient and advanced way for classifying and intelligent retrieval of medical imaging. This research work is to classify human organs based on MRI images. The various MRI images of organ have been considered as the data set. The main objective of this research work is to automate the medical imaging system. Digital images retrieved based on its shape by Canny Edge Detection and is clustered together in one class using K-Means Algorithm. 2564 data sets related to brain and heart is considered for this research work. The system was trained to classify the image which results in faster execution in medical field, also helped in obtain noiseless and efficient data.

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Cho, Kyungwoon, and Hyokyung Bahn. "Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms." Applied Sciences 15, no. 8 (2025): 4539. https://doi.org/10.3390/app15084539.

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Image filtering systems have become essential in large-scale content platforms to prevent the dissemination of unauthorized data. While extensive research has focused on identifying images based on categories or visual similarity, the filtering problem addressed in this study presents distinct challenges. Specifically, it involves a predefined set of filtering images and requires real-time detection of whether a distributed image is derived from an unauthorized source. Although three major approaches—bitmap-based, image processing-based, and deep learning-based techniques—have been explored, no comprehensive comparison has been conducted. To bridge this gap, we formalize the concept of image equivalence and introduce performance metrics tailored for fair evaluation. Through extensive experiments, we derive the following key findings. First, bitmap-based methods are practically viable in real-world scenarios, offering reasonable detection rates and fast search speeds even under resource constraints. Second, despite their success in tasks such as image classification, deep learning-based methods underperform in our problem domain, highlighting the need for customized models and architectures. Third, image processing-based techniques demonstrate superior performance across all key metrics, including execution time and detection rates. These findings provide valuable insights into designing efficient image filtering systems for diverse content platforms, particularly for detecting unauthorized images and their transformations effectively.

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Liu, Xian Li, Xiao Ran Song, L. J. Liu, Zhong Yang Zhao, and D. L. Ma. "Large NC Machining Fast Knife Mould Based on Image Technology." Advanced Materials Research 188 (March 2011): 613–16. http://dx.doi.org/10.4028/www.scientific.net/amr.188.613.

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In NC machining large moulds, second mold will install deviated orientation problem. Based on image technology, after the second for installation of workpiece, collected clip in the concrete pins on measuring and calculating the image processing, analysis, and finally got the localization generated during installation and adjustment, the deviation of the machine to eliminate biases. In image processing of PSCP thinning algorithm, based on the characteristics of image edge detection were analyzed, the extraction and processing, improve the machining accuracy and efficiency. This method can also be used in small parts processing detection, etc.

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Swarajya Lakshmi, B. "Fire Detection Using Image Processing." Asian Journal of Computer Science and Technology 10, no. 2 (2021): 14–19. http://dx.doi.org/10.51983/ajcst-2021.10.2.2883.

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Fire disasters have always been a threat to homes and businesses even with the various systems in place to prevent them. They cause property damage, injuries and even death. Preparedness is vital when dealing with fires. They spread uncontrollably and are difficult to contain. To contain them it is necessary for the fire to be detected early. Image fire detection heavily relies on an algorithmic analysis of images. However, the accuracy is lower, the detection is delayed and in common detection algorithms a large number of computation, including the image features being extracted manually and using machine. Therefore, in this paper, novel image detection which will be based on the advanced object detection like CNN model of YOLO v3 is proposed. The average precision of the algorithm based on YOLO v3 reaches to 81.76% and also it has the stronger robustness of detection performance, thereby satisfying the requirements of the real-time detection.

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Dr., Glory Sujitha Antony, and Deepan Arulanandham Amala. "An Examination of Image Mining: Approaches, Techniques, and Application." An Examination of Image Mining: Approaches, Techniques, and Application 9, no. 1 (2024): 4. https://doi.org/10.5281/zenodo.10628880.

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Image mining, encompassing Image processing, Databases, Data mining, Machine learning, and artificial Intelligence, is primarily focused on the extraction of patterns from extensive collections of images. Despite the considerable amount of research conducted in these domains, challenges persist within the field of image mining. For example, data mining techniques are unable to automate the extraction of information from large sets of images. Within this paper, we explore a comprehensive approach to extract data based on the existing body of research. This paper aims to assist readers interested in gaining insights into current image mining techniques and advancing their knowledge extraction from substantial image datasets within this domain. Keywords:- Data Mining, Image Processing, Neural Network, Machine Learning, Image Mining, Image Classification, Image Clustering, Image Pre-Processing, And Content-Based Image Retrieval (CBIR).

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Li, Weijia, Conghui He, Haohuan Fu, et al. "A Real-Time Tree Crown Detection Approach for Large-Scale Remote Sensing Images on FPGAs." Remote Sensing 11, no. 9 (2019): 1025. http://dx.doi.org/10.3390/rs11091025.

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The on-board real-time tree crown detection from high-resolution remote sensing images is beneficial for avoiding the delay between data acquisition and processing, reducing the quantity of data transmission from the satellite to the ground, monitoring the growing condition of individual trees, and discovering the damage of trees as early as possible, etc. Existing high performance platform based tree crown detection studies either focus on processing images in a small size or suffer from high power consumption or slow processing speed. In this paper, we propose the first FPGA-based real-time tree crown detection approach for large-scale satellite images. A pipelined-friendly and resource-economic tree crown detection algorithm (PF-TCD) is designed through reconstructing and modifying the workflow of the original algorithm into three computational kernels on FPGAs. Compared with the well-optimized software implementation of the original algorithm on an Intel 12-core CPU, our proposed PF-TCD obtains the speedup of 18.75 times for a satellite image with a size of 12,188 × 12,576 pixels without reducing the detection accuracy. The image processing time for the large-scale remote sensing image is only 0.33 s, which satisfies the requirements of the on-board real-time data processing on satellites.

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Yang, Li Juan, Pei Huang Lou, and Xiao Ming Qian. "Recognition of initial welding position for large diameter pipeline based on pulse coupled neural network." Industrial Robot: An International Journal 42, no. 4 (2015): 339–46. http://dx.doi.org/10.1108/ir-01-2015-0011.

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Purpose – The main purpose of this paper is to develop a method to recognize the initial welding position for large-diameter pipeline automatically, and introduce the image processing based on pulse-coupled neural network (PCNN) which is adopted by the proposed method. Design/methodology/approach – In this paper, a passive vision sensor is designed to capture weld seam images in real time. The proposed method contains two steps. The first step is to detect the rough position of the weld seam, and the second step is to recognize one of the solder joints from the local image and extract its centroid, which is regarded as the initial welding position. In each step, image segmentation and removal of small false regions based on PCNN are adopted to obtain the object regions; then, the traditional image processing theory is used for the subsequent processing. Findings – The experimental results show the feasibility and real time of the proposed method. Based on vision sensing technology and PCNN, it is able to achieve the autonomous recognition of initial welding position in large-diameter pipeline welding. Practical implications – The proposed method can greatly shorten the time of positioning the initial welding position and satisfy the automatic welding for large-diameter pipeline. Originality/value – In the proposed method, the image pre-processing is based on PCNN, which is more robust and flexible in the complex welding environment. After that, traditional image processing theory is adopted for the subsequent processing, of which the processing speed is faster.

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Liu, Qi, Yu Lan Wei, Bing Li, Meng Dan Jin, and Ying Ying Fan. "Detection Devices and Technologies on Large-Scale Pipe Weld Surface Defect." Advanced Materials Research 580 (October 2012): 445–48. http://dx.doi.org/10.4028/www.scientific.net/amr.580.445.

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The kind and extent of defect can be identified through image processing. First, the weld defect detection device should be constructed, and then the defect imaged should be obtained through rational way, in order to enhance the image quality, image filter and image enhancement method should be use. To ensure the real-time system, the weld region need to segment from the image. After that, the needed defect features need to determine and extract. Finally, the kind, the location and the size of the defect can be defined.

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Tosi, Sébastien, Lídia Bardia, Maria Jose Filgueira, Alexandre Calon, and Julien Colombelli. "LOBSTER: an environment to design bioimage analysis workflows for large and complex fluorescence microscopy data." Bioinformatics 36, no. 8 (2019): 2634–35. http://dx.doi.org/10.1093/bioinformatics/btz945.

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Abstract Summary Open source software such as ImageJ and CellProfiler greatly simplified the quantitative analysis of microscopy images but their applicability is limited by the size, dimensionality and complexity of the images under study. In contrast, software optimized for the needs of specific research projects can overcome these limitations, but they may be harder to find, set up and customize to different needs. Overall, the analysis of large, complex, microscopy images is hence still a critical bottleneck for many Life Scientists. We introduce LOBSTER (Little Objects Segmentation and Tracking Environment), an environment designed to help scientists design and customize image analysis workflows to accurately characterize biological objects from a broad range of fluorescence microscopy images, including large images exceeding workstation main memory. LOBSTER comes with a starting set of over 75 sample image analysis workflows and associated images stemming from state-of-the-art image-based research projects. Availability and implementation LOBSTER requires MATLAB (version ≥ 2015a), MATLAB Image processing toolbox, and MATLAB statistics and machine learning toolbox. Code source, online tutorials, video demonstrations, documentation and sample images are freely available from: https://sebastients.github.io. Supplementary information Supplementary data are available at Bioinformatics online.

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Khalaf, Walaa, Abeer Al Gburi, and Dhafer Zaghar. "Pre and Postprocessing for JPEG to Handle Large Monochrome Images." Algorithms 12, no. 12 (2019): 255. http://dx.doi.org/10.3390/a12120255.

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Image compression is one of the most important fields of image processing. Because of the rapid development of image acquisition which will increase the image size, and in turn requires bigger storage space. JPEG has been considered as the most famous and applicable algorithm for image compression; however, it has shortfalls for some image types. Hence, new techniques are required to improve the quality of reconstructed images as well as to increase the compression ratio. The work in this paper introduces a scheme to enhance the JPEG algorithm. The proposed scheme is a new method which shrinks and stretches images using a smooth filter. In order to remove the blurring artifact which would be developed from shrinking and stretching the image, a hyperbolic function (tanh) is used to enhance the quality of the reconstructed image. Furthermore, the new approach achieves higher compression ratio for the same image quality, and/or better image quality for the same compression ratio than ordinary JPEG with respect to large size and more complex content images. However, it is an application for optimization to enhance the quality (PSNR and SSIM), of the reconstructed image and to reduce the size of the compressed image, especially for large size images.

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Filiberti, Daniel P., Paolo Bellutta, Phillip Ngan, and Douglas A. Perednia. "Efficient segmentation of large-area skin images: an overview of image processing." Skin Research and Technology 1, no. 4 (1995): 200–208. http://dx.doi.org/10.1111/j.1600-0846.1995.tb00044.x.

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Mader, S., and G. J. Grenzdörffer. "AUTOMATIC SEA BIRD DETECTION FROM HIGH RESOLUTION AERIAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 299–303. http://dx.doi.org/10.5194/isprs-archives-xli-b7-299-2016.

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Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted sub-images. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.

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