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Relevant bibliographies by topics / Application to image restoration

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Academic literature on the topic 'Application to image restoration'

Author: Grafiati

Published: 12 April 2025

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Journal articles on the topic "Application to image restoration"

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Ishii, Shin, Sehyung Lee, Hidetoshi Urakubo, Hideaki Kume, and Haruo Kasai. "Generative and discriminative model-based approaches to microscopic image restoration and segmentation." Microscopy 69, no. 2 (March 26, 2020): 79–91. http://dx.doi.org/10.1093/jmicro/dfaa007.

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Abstract Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.

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Tang, Yi, Jin Qiu, and Ming Gao. "Fuzzy Medical Computer Vision Image Restoration and Visual Application." Computational and Mathematical Methods in Medicine 2022 (June 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/6454550.

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In order to shorten the image registration time and improve the imaging quality, this paper proposes a fuzzy medical computer vision image information recovery algorithm based on the fuzzy sparse representation algorithm. Firstly, by constructing a computer vision image acquisition model, the visual feature quantity of the fuzzy medical computer vision image is extracted, and the feature registration design of the fuzzy medical computer vision image is carried out by using the 3D visual reconstruction technology. Then, by establishing a multidimensional histogram structure model, the wavelet multidimensional scale feature detection method is used to achieve grayscale feature extraction of fuzzy medical computer vision images. Finally, the fuzzy sparse representation algorithm is used to automatically optimize the fuzzy medical computer vision images. The experimental results show that the proposed method has a short image information registration time, less than 10 ms, and has a high peak PSNR. When the number of pixels is 700, its peak PSNR can reach 83.5 dB, which is suitable for computer image restoration.

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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 (June 11, 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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Liu, Zihan. "Literature Review on Image Restoration." Journal of Physics: Conference Series 2386, no. 1 (December 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2386/1/012041.

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Abstract Image restoration is an essential part in the field of computer vision, which aims at predicting and filling the pixels of the missing images to achieve satisfactory visual effects, it has extensive application value in the fields of film and television special effects production, image editing, digital cultural heritage protection and virtual reality. With the introduction and application of the concept of deep learning in recent years, it has been widely studied in the academic and industrial fields, the performance of image restoration has been significantly improved, so that this long-standing research topic has once again aroused widespread concern and heated discussion on the social level. In order to enable more researchers to explore the theory of image restoration and its development, this paper reviews the related technologies in this field: firstly, the traditional image restoration methods are described, secondly, the background of deep learning is introduced, then the image restoration methods based on deep learning are described, subsequently, the several deep-learning based methods are compared and analyzed, finally, the future research direction and emphasis of image restoration are analyzed and prospected.

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Yuan, Yuan, Yao Hua Yi, and Min Jing Miao. "An Automatic Calculation Method of MTF and the Application in Blurred Images Restoration." Applied Mechanics and Materials 731 (January 2015): 141–46. http://dx.doi.org/10.4028/www.scientific.net/amm.731.141.

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Modulation transfer function (MTF) is a comprehensive index for the objective reflection of the quality of the imaging systems. In the field of image processing, the slanted-edge method is usually adopted to compute MTF for images. However, the sub-images with slanted edges are extracted from original image by subjective judgment and manually operation, which is bound to lead to inefficiency of calculation, low accuracy and instability of results with the participation of humans. Aiming at the above problem, this paper presents an automatic MTF calculation method for blurred images and applies it to the process of image restoration by developing a two-dimensional MTF filter and utilizing it into conventional restoration methods such as the Wiener filtering, least squares filtering and Lucy-Richardson algorithm. Experiment results indicate the proposed method achieved an automatic, fast and accurate MTF computation for blurred images, and MTF-based restoration methods were superior to traditional ones in restoration effects.

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Li, Yiyang. "Digital signal processing techniques for image enhancement and restoration." Applied and Computational Engineering 17, no. 1 (October 23, 2023): 198–205. http://dx.doi.org/10.54254/2755-2721/17/20230940.

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Digital image processing has become a fundamental tool in modern image processing, including image enhancement and restoration. This paper reviews important image enhancement and restoration techniques in digital image processing. First, some important image enhancement techniques such as histogram equalization are introduced and compared in detail, including their advantages, disadvantages, and application scenarios. Secondly, for image restoration techniques, this paper introduces deblurring techniques such as deconvolution and blind deconvolution, explaining their working principles and application scenarios in detail. Finally, this paper introduces the development and applications of super-resolution technology, and explores their possible future development directions. This review provides comprehensive technical references for researchers in digital image processing.

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Hafiz Muhammad Tayyab Khushi. "Impulse Noise Removal Using Soft-computing." Lahore Garrison University Research Journal of Computer Science and Information Technology 6, no. 1 (March 30, 2022): 32–48. http://dx.doi.org/10.54692/lgurjcsit.2022.0601275.

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Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation.

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Kashyap, R. L., and K. B. Eom. "Robust image modeling techniques with an image restoration application." IEEE Transactions on Acoustics, Speech, and Signal Processing 36, no. 8 (1988): 1313–25. http://dx.doi.org/10.1109/29.1659.

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Hu, Yang Bo, Hua Jiang, and Long Bing Li. "The Research of Application in Image Restoration Based on Wiener Filtering." Applied Mechanics and Materials 278-280 (January 2013): 1232–36. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1232.

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Image, due to the impact of such as blurring, distortion, noise, etc., will cause a reduction in image quality and the formation of the degradation of the digital image. The using of some kind of a priori knowledge use the least squares method to be filtered, so that the original image and its recovery minimum mean square error between the two images. This can get a better recovery image.

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Tao, Yu, and Jan-Peter Muller. "Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System." Remote Sensing 11, no. 1 (December 29, 2018): 52. http://dx.doi.org/10.3390/rs11010052.

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High spatial resolution Earth observation imagery is considered desirable for many scientific and commercial applications. Given repeat multi-angle imagery, an imaging instrument with a specified spatial resolution, we can use image processing and deep learning techniques to enhance the spatial resolution. In this paper, we introduce the University College London (UCL) MAGiGAN super-resolution restoration (SRR) system based on multi-angle feature restoration and deep SRR networks. We explore the application of MAGiGAN SRR to a set of 9 MISR red band images (275 m) to produce up to a factor of 3.75 times resolution enhancement. We show SRR results over four different test sites containing different types of image content including urban and rural targets, sea ice and a cloud field. Different image metrics are introduced to assess the overall SRR performance, and these are employed to compare the SRR results with the original MISR input images and higher resolution Landsat images, where available. Significant resolution improvement over various types of image content is demonstrated and the potential of SRR for different scientific application is discussed.

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Dissertations / Theses on the topic "Application to image restoration"

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Boukouvala, Erisso. "Image restoration techniques and application on astronomical images." Thesis, University of Reading, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414571.

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Qiu, Zhen. "Feature-preserving image restoration and its application in biological fluorescence microscopy." Thesis, Heriot-Watt University, 2013. http://hdl.handle.net/10399/2682.

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This thesis presents a new investigation of image restoration and its application to fluorescence cell microscopy. The first part of the work is to develop advanced image denoising algorithms to restore images from noisy observations by using a novel featurepreserving diffusion approach. I have applied these algorithms to different types of images, including biometric, biological and natural images, and demonstrated their superior performance for noise removal and feature preservation, compared to several state of the art methods. In the second part of my work, I explore a novel, simple and inexpensive super-resolution restoration method for quantitative microscopy in cell biology. In this method, a super-resolution image is restored, through an inverse process, by using multiple diffraction-limited (low) resolution observations, which are acquired from conventional microscopes whilst translating the sample parallel to the image plane, so referred to as translation microscopy (TRAM). A key to this new development is the integration of a robust feature detector, developed in the first part, to the inverse process to restore high resolution images well above the diffraction limit in the presence of strong noise. TRAM is a post-image acquisition computational method and can be implemented with any microscope. Experiments show a nearly 7-fold increase in lateral spatial resolution in noisy biological environments, delivering multi-colour image resolution of ~30 nm.

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Abboud, Feriel. "Restoration super-resolution of image sequences : application to TV archive documents." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1038/document.

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Au cours du dernier siècle, le volume de vidéos stockées chez des organismes tel que l'Institut National de l'Audiovisuel a connu un grand accroissement. Ces organismes ont pour mission de préserver et de promouvoir ces contenus, car, au-delà de leur importance culturelle, ces derniers ont une vraie valeur commerciale grâce à leur exploitation par divers médias. Cependant, la qualité visuelle des vidéos est souvent moindre comparée à celles acquises par les récents modèles de caméras. Ainsi, le but de cette thèse est de développer de nouvelles méthodes de restauration de séquences vidéo provenant des archives de télévision française, grâce à de récentes techniques d'optimisation. La plupart des problèmes de restauration peuvent être résolus en les formulant comme des problèmes d'optimisation, qui font intervenir plusieurs fonctions convexes mais non-nécessairement différentiables. Pour ce type de problèmes, on a souvent recourt à un outil efficace appelé opérateur proximal. Le calcul de l'opérateur proximal d'une fonction se fait de façon explicite quand cette dernière est simple. Par contre, quand elle est plus complexe ou fait intervenir des opérateurs linéaires, le calcul de l'opérateur proximal devient plus compliqué et se fait généralement à l'aide d'algorithmes itératifs. Une première contribution de cette thèse consiste à calculer l'opérateur proximal d'une somme de plusieurs fonctions convexes composées avec des opérateurs linéaires. Nous proposons un nouvel algorithme d'optimisation de type primal-dual, que nous avons nommé Algorithme Explicite-Implicite Dual par Blocs. L'algorithme proposé permet de ne mettre à jour qu'un sous-ensemble de blocs choisi selon une règle déterministe acyclique. Des résultats de convergence ont été établis pour les deux suites primales et duales de notre algorithme. Nous avons appliqué notre algorithme au problème de déconvolution et désentrelacement de séquences vidéo. Pour cela, nous avons modélisé notre problème sous la forme d'un problème d'optimisation dont la solution est obtenue à l'aide de l'algorithme explicite-implicite dual par blocs. Dans la deuxième partie de cette thèse, nous nous sommes intéressés au développement d'une version asynchrone de notre l'algorithme explicite-implicite dual par blocs. Dans cette nouvelle extension, chaque fonction est considérée comme locale et rattachée à une unité de calcul. Ces unités de calcul traitent les fonctions de façon indépendante les unes des autres. Afin d'obtenir une solution de consensus, il est nécessaire d'établir une stratégie de communication efficace. Un point crucial dans le développement d'un tel algorithme est le choix de la fréquence et du volume de données à échanger entre les unités de calcul, dans le but de préserver de bonnes performances d'accélération. Nous avons évalué numériquement notre algorithme distribué sur un problème de débruitage de séquences vidéo. Les images composant la vidéo sont partitionnées de façon équitable, puis chaque processeur exécute une instance de l'algorithme de façon asynchrone et communique avec les processeurs voisins. Finalement, nous nous sommes intéressés au problème de déconvolution aveugle, qui vise à estimer le noyau de convolution et la séquence originale à partir de la séquence dégradée observée. Nous avons proposé une nouvelle méthode basée sur la formulation d'un problème non-convexe, résolu par un algorithme itératif qui alterne entre l'estimation de la séquence originale et l'identification du noyau. Notre méthode a la particularité de pouvoir intégrer divers types de fonctions de régularisations avec des propriétés mathématiques différentes. Nous avons réalisé des simulations sur des séquences synthétiques et réelles, avec différents noyaux de convolution. La flexibilité de notre approche nous a permis de réaliser des comparaisons entre plusieurs fonctions de régularisation convexes et non-convexes, en terme de qualité d'estimation
The last century has witnessed an explosion in the amount of video data stored with holders such as the National Audiovisual Institute whose mission is to preserve and promote the content of French broadcast programs. The cultural impact of these records, their value is increased due to commercial reexploitation through recent visual media. However, the perceived quality of the old data fails to satisfy the current public demand. The purpose of this thesis is to propose new methods for restoring video sequences supplied from television archive documents, using modern optimization techniques with proven convergence properties. In a large number of restoration issues, the underlying optimization problem is made up with several functions which might be convex and non-necessarily smooth. In such instance, the proximity operator, a fundamental concept in convex analysis, appears as the most appropriate tool. These functions may also involve arbitrary linear operators that need to be inverted in a number of optimization algorithms. In this spirit, we developed a new primal-dual algorithm for computing non-explicit proximity operators based on forward-backward iterations. The proposed algorithm is accelerated thanks to the introduction of a preconditioning strategy and a block-coordinate approach in which at each iteration, only a "block" of data is selected and processed according to a quasi-cyclic rule. This approach is well suited to large-scale problems since it reduces the memory requirements and accelerates the convergence speed, as illustrated by some experiments in deconvolution and deinterlacing of video sequences. Afterwards, a close attention is paid to the study of distributed algorithms on both theoretical and practical viewpoints. We proposed an asynchronous extension of the dual forward-backward algorithm, that can be efficiently implemented on a multi-cores architecture. In our distributed scheme, the primal and dual variables are considered as private and spread over multiple computing units, that operate independently one from another. Nevertheless, communication between these units following a predefined strategy is required in order to ensure the convergence toward a consensus solution. We also address in this thesis the problem of blind video deconvolution that consists in inferring from an input degraded video sequence, both the blur filter and a sharp video sequence. Hence, a solution can be reached by resorting to nonconvex optimization methods that estimate alternatively the unknown video and the unknown kernel. In this context, we proposed a new blind deconvolution method that allows us to implement numerous convex and nonconvex regularization strategies, which are widely employed in signal and image processing

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Al-Suwailem, Umar A. "Continuous spatial domain image identification and restoration with multichannel applications /." free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9737865.

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Auyeung, Cheung. "Optimal constraint-based signal restoration and its applications." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/15785.

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Eastlick, Anne C. "Genre criticism : an application of BP's image restoration campaign to the crisis communication genre." Scholarly Commons, 2011. https://scholarlycommons.pacific.edu/uop_etds/767.

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Within two months of its emergence, the BP Gulf Oil spill had become the worst environmental disaster in United States history. However, for those studying public relations the oil spill brought more than ecological disaster, by providing a case study of crisis communication. Although there were a number of crisis responses from BP throughout the course of the oil spill, the primary crisis response crafted by BP was an image restoration campaign which premiered in early June 2010. This campaign, though it exhibits qualities of a standard crisis response, was wildly unpopular with the United States Government and citizenry. This rhetorical analysis attempts to uncover the reasons behind the campaign's failure through an application of the genre model of criticism. By defining the crisis communication genre and applying it to the artifact, the current study uncovers the reasons behind the failure of the campaign. Through this discussion, this analysis identifies that BP did not address all necessary exigencies, nor did it consider the influence a rhetor can have on a message. An explanation for the failure of BP' s campaign provided a plethora of implications to the fields of public . relations and rhetorical criticism, while beginning a discussion to help define the crisis communication genre.

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Wen, Youwei. "Fast solvers for Toeplitz systems with applications to image restoration." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B3688280X.

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Wen, Youwei, and 文有為. "Fast solvers for Toeplitz systems with applications to image restoration." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B3688280X.

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Saeed, Mohammed. "Maximum likelihood parameter estimation of mixture models and its application to image segmentation and restoration." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43410.

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Gibbs, Alison L. "Convergence of Markov chain Monte Carlo algorithms with applications to image restoration." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ50003.pdf.

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Books on the topic "Application to image restoration"

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Zelensky, Alexander A., 1943- author and Kravchenko, Viktor F., 1939- author, eds. Bispectral methods of signal processing: Applications in radar, telecommunications and digital image restoration. Berlin: Walter de Gruyter GmbH & Co. KG, 2015.

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Favaro, Paolo. 3-D shape estimation and image restoration: Exploiting defocus and motion blur. London: Springer, 2007.

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Lesk, Michael. Image formats for preservation and access: A report. Washington, D.C: Commission on Preservation and Access, 1990.

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Dobreva, Milena P. Applications of computer tools in studying medieval Slavonic manuscripts. Sofia, Bulgaria: Boyko Kacharmazov, 1995.

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Katsaggelos, Aggelos K., ed. Digital Image Restoration. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-58216-5.

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1956-, Katsaggelos Aggelos Konstantinos, ed. Digital image restoration. Berlin: Springer-Verlag, 1991.

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J, McDonnell M., ed. Image restoration and reconstruction. Oxford [Oxfordshire]: Clarendon Press, 1986.

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Hunter, Michael. The Image of Restoration Science. London ; New York : Routledge, 2017.: Routledge, 2016. http://dx.doi.org/10.4324/9781315556857.

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Olson, Rex. Professional Photoshop: Image restoration & repair. Burbank, Calif: Desktop Images, 2002.

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Rütimann, Hans. Computerization project of the Archivo General de Indias, Seville, Spain: A report to the Commission on Preservation and Access. Washington, D.C: Commission on Preservation and Access, 1992.

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Book chapters on the topic "Application to image restoration"

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Moura Neto, Francisco Duarte, and Antônio José da Silva Neto. "Image Restoration." In An Introduction to Inverse Problems with Applications, 85–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32557-1_5.

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Averbuch, Amir Z., Pekka Neittaanmaki, and Valery A. Zheludev. "Application of Periodic Frames to Image Restoration." In Spline and Spline Wavelet Methods with Applications to Signal and Image Processing, 465–78. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-8926-4_18.

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Hu, Wenjin, Fuliang Zen, Jiahao Meng, and Yuqi Ye. "Digital Restoration for Damaged Thangka Image." In Application of Intelligent Systems in Multi-modal Information Analytics, 857–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15740-1_112.

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Ghennam, Souheila, and Khier Benmahammed. "Image Restoration Using Neural Networks." In Bio-Inspired Applications of Connectionism, 227–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45723-2_27.

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González-Jaime, Luis, Mike Nachtegeal, Etienne Kerre, Gonzalo Vegas-Sánchez-Ferrero, and Santiago Aja-Fernández. "Parametric Image Restoration Using Consensus: An Application to Nonstationary Noise Filtering." In Pattern Recognition and Image Analysis, 358–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_42.

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He, Chuan, and Changhua Hu. "Parallel Primal-dual Method with Application to Image Restoration." In Parallel Operator Splitting Algorithms with Application to Imaging Inverse Problems, 141–88. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3750-9_6.

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Diffellah, Nacira, Rabah Hamdini, and Tewfik Bekkouche. "Image Restoration Using Proximal-Splitting Methods." In Artificial Intelligence and Its Applications, 437–46. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96311-8_40.

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Rooms, Filip, Bart Goossens, Aleksandra Pižurica, and Wilfried Philips. "Image Restoration and Applications in Biomedical Processing." In Optical and Digital Image Processing, 571–91. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527635245.ch26.

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He, Chuan, and Changhua Hu. "Fast Parameter Estimation in TV-Based Image Restoration." In Parallel Operator Splitting Algorithms with Application to Imaging Inverse Problems, 73–105. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3750-9_4.

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Sarrafzadeh, M., A. K. Katsaggelos, and S. P. R. Kumar. "Parallel Architectures For Iterative Image Restoration." In Parallel Algorithms and Architectures for DSP Applications, 1–31. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3996-4_1.

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Conference papers on the topic "Application to image restoration"

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Yang, Lei, Jingyi Liu, Ze Shi, and Caijuan Shi. "SCMamba: A Space Correction State Space Model for Image Restoration." In 2024 7th International Conference on Computer Information Science and Application Technology (CISAT), 436–40. IEEE, 2024. http://dx.doi.org/10.1109/cisat62382.2024.10695207.

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Cheng, B. T., M. A. Fiddy, J. D. Newman, R. C. Van Vranken, and D. L. Clark. "Image restoration from low light level degraded data." In Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/qlip.1989.tuc4.

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We present some preliminary work on the reconstruction of low contrast images for remote sensing type applications. We assume the data to be a set of noise degraded images, and report on the application of reconstruction techniques that both estimate the support of the image use the triple correlation method to obtain the image itself. These reconstruction methods are applied to simulated data in the first instance.

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Wang, Fu, and Lin Deng. "The Application of Image Restoration in Aviation Image." In 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/iccmcee-15.2015.157.

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Sezan, M. Ibrahim. "Method of convex projections for image enhancement and restoration." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.mf1.

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The method of projections onto convex sets (POCS) is an interative method that finds a feasible solution consistent with a number of a priori constraints. POCS has found applications in numerous fields ranging from astronomy to neural networks. Here we focus on the application of POCS in image restoration and enhancement. In image restoration and enhancement problems, a priori constraints are defined on the basis of the measured data as well as on the degradation operator, the noise statistics, and the actual image distribution itself. For each constraint, a closed convex set is defined. An estimate of the actual image distribution is defined as a member of the intersection set and is determined by successively projecting an initial estimate onto the constraint sets. After a brief review of the fundamentals of POCS, we discuss the application of POCS to two problems: (i) restoration of images degraded by both blur and noise, and (ii) resolution enhancement of image sequences by reconstructing high-resolution still frames from a sequence of images at a lower spatial resolution.

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Okitsu, Nagayuki, and Masato Shirai. "Spatial Attention for Image Restoration." In International Conference on Industrial Application Engineering 2024. The Institute of Industrial Applications Engineers, 2024. http://dx.doi.org/10.12792/iciae2024.042.

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Lambert, Andrew J., James Webb, and Donald Fraser. "Fast intelligent image sensor with application to image restoration." In International Symposium on Optical Science and Technology, edited by C. Bruce Johnson, Divyendu Sinha, and Phillip A. Laplante. SPIE, 2003. http://dx.doi.org/10.1117/12.452134.

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Lakshmi, A., and Subrata Rakshit. "Gaussian Restoration pyramid : Application of image restoration to Laplacian pyramid compression." In 2010 IEEE 2nd International Advance Computing Conference (IACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iadcc.2010.5423035.

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Yu Hua, Wu Wen-Quan, and Liu Zhong. "Application of Toeplitz matrix in image restoration." In 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2010. http://dx.doi.org/10.1109/bicta.2010.5645155.

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Pandey, Mukesh, Gunjan Rawat, and Puneet Kanti. "Image Restoration Application and Methods for Different Images: A Review." In 2022 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2022. http://dx.doi.org/10.1109/icaccm56405.2022.10009397.

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Hong Sun, H. Maitre, and Bao Guan. "Turbo image restoration." In Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isspa.2003.1224729.

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Reports on the topic "Application to image restoration"

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Carasso, Alfred S., and András E. Vladár. Calibrating image roughness by estimating Lipschitz exponents, with application to image restoration. Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7438.

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Lal, Anisha M., Ali A. Abdulla, and Aju Dennisan. Remote Sensing Image Restoration for Environmental Applications Using Estimated Parameters. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, August 2018. http://dx.doi.org/10.7546/crabs.2018.08.11.

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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.

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Jennison, Christopher, and Michael Jubb. Statistical Image Restoration and Refinement. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada196142.

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Murphy, P. K. Survey of Image Restoration Techniques. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada197470.

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Chan, Tony F., and Jianhong Shen. A Good Image Model Eases Restoration. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada437474.

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Goda, Matthew E. Wavelet Domain Image Restoration and Super-Resolution. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada405111.

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Mairal, Julien, Michael Elad, and Guillermo Sapiro. Sparse Representation for Color Image Restoration (PREPRINT). Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada478437.

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Jefferies, Stuart M., Douglas A. Hope, and C. A. Giebink. Next Generation Image Restoration for Space Situational Awareness. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada495284.

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Barbacci, Mario R., and Dennis L. Doubleday. Generalized Image Library: A Durra Application Example. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada199481.

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