Relevant bibliographies by topics / Inpainting Algorithms

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Academic literature on the topic 'Inpainting Algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Inpainting Algorithms"

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Liu, Jiansheng, Hui Liu, Shangping Qiao, and Guangxue Yue. "An Automatic Image Inpainting Algorithm Based on FCM." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/201704.

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There are many existing image inpainting algorithms in which the repaired area should be manually determined by users. Aiming at this drawback of the traditional image inpainting algorithms, this paper proposes an automatic image inpainting algorithm which automatically identifies the repaired area by fuzzy C-mean (FCM) algorithm. FCM algorithm classifies the image pixels into a number of categories according to the similarity principle, making the similar pixels clustering into the same category as possible. According to the provided gray value of the pixels to be inpainted, we calculate the
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Sreelakshmy, I. J., and C. Kovoor Binsu. "Towards Source-Based Classification of Image Inpainting Techniques: A Survey." Journal of Information & Knowledge Management 20, no. 03 (2021): 2150039. http://dx.doi.org/10.1142/s0219649221500398.

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Image inpainting is a process of reconstructing an incomplete image from the available information in a visually plausible way. In the proposed framework, existing image inpainting methods are classified in a new perspective. The information which is referred to, while reconstructing an image, is a critical factor of inpainting algorithms. Source of this information can be host image itself or an external source. The proposed framework broadly classifies inpainting algorithms into introspective and extrospective categories based on the source of information. Various parameters influencing the
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Et. al., Shweta Dhondse. "Intuitionistic Fuzzy Sets Based Inpainting for Reconstruction of Heritage Images." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 5556–65. http://dx.doi.org/10.17762/turcomat.v12i3.2219.

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Image inpainting is a process of reconstructing damaged or missing part of an image. Initially the work of restoration was performed by skilled artists and was limited to paintings and other artwork of eminence but this process was very tedious and strenuous and therefore digital image inpainting was introduced. The application of inpainting in reconstruction of heritage images is garnering a lot of attention from researchers. The reconstruction of heritage images using inpainting poses a challenging task because of its very high resolution and high meaning full structure content. In tradition
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Zhao, Junhong, Jintao Tan, Yaobin Huang, and Chuanlong Lu. "Improved image inpainting exemplar-based algorithms by boundary priori-knowledge." MATEC Web of Conferences 355 (2022): 03004. http://dx.doi.org/10.1051/matecconf/202235503004.

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Image inpainting plays an important role in restoration of cultural relics, pictures beautification. Criminisi algorithm creates good results in large-area inpainting. However, it does still have some deficiencies such as over-extending. In this paper, two improved algorithms based on prior knowledge of the boundary had been proposed by simulating the idea of manual repairing. An algorithm, by simulating the strategy that the next inpainted pixel will be near to the prior one, named nearer neighbor first algorithm, can void the random bounding of the to-be-inpainted pixle. Another algorithm, b
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Yatnalli, V., B. G. Shivaleelavathi, and K. L. Sudha. "Review of Inpainting Algorithms for Wireless Communication Application." Engineering, Technology & Applied Science Research 10, no. 3 (2020): 5790–95. http://dx.doi.org/10.48084/etasr.3547.

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Digital image inpainting is a technique of restoring large removed /damaged regions of an image with the data from the surrounding pixels of the removed region. The issue of image restoration with inpainting techniques occurs commonly in computer vision/image processing when unwanted objects have to be removed from images, for filling cracks in photographs, etc. Digital image inpainting approach is an active field of research in two significant applications of wireless communication: image compression and image recovery from a damaged image due to errors in a wireless channel. This work presen
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Gao, Jingjing. "Generative adversarial network based image inpainting." Applied and Computational Engineering 5, no. 1 (2023): 93–98. http://dx.doi.org/10.54254/2755-2721/5/20230540.

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Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are
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Shen, Yankun, Yaya Su, Lin Wang, and Dongli Jia. "Research on Image Inpainting Algorithms Based on Attention Guidance." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 2 (2023): 190–97. http://dx.doi.org/10.20965/jaciii.2023.p0190.

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In recent years, the use of deep learning in image inpainting has yielded positive results. However, existing image inpainting algorithms do not pay sufficient attention to the structural and textural features of the image when inpainting, which leads to issues in the inpainting results such as blurring and distortion. To solve the above problems, a channel attention mechanism was introduced to emphasize the importance of structure and texture after extraction by the convolutional network. A bidirectional gated feature fusion module was employed to exchange and fuse the structural and textural
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Kwan, Chiman, and Jude Larkin. "Further Improvements of CFA 3.0 by Combining Inpainting and Pansharpening Techniques." Signal & Image Processing : An International Journal 11, no. 6 (2020): 1–19. http://dx.doi.org/10.5121/sipij.2020.11601.

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Color Filter Array (CFA) has been widely used in digital cameras. There are many variants of CFAs in the literature. Recently, a new CFA known as CFA 3.0 was proposed by us and has been shown to yield reasonable performance as compared to some standard ones. In this paper, we investigate the use of inpainting algorithms to further improve the demosaicing performance of CFA 3.0. Six conventional and deep learning based inpainting algorithms were compared. Extensive experiments demonstrated that one algorithm improved over other approaches.
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Song, Xiaorui, and Lingda Wu. "Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning." Applied Sciences 9, no. 15 (2019): 3062. http://dx.doi.org/10.3390/app9153062.

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To address the problems of defective pixels and strips in hyperspectral images affecting subsequent processing and applications, we modeled the hyperspectral image (HSI) inpainting problem as a sparse signal reconstruction problem with incomplete observations using the theory of sparse representation, and proposed an HSI inpainting algorithm based on spectral dictionary learning. First, we studied the HSI observation model under the assumption of additive noise. We subsequently proposed a new algorithm for constructing a spectral dictionary directly from hyperspectral data by introducing an on
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Janardhana Rao, B., and O. Venkata Krishna. "Evaluation of Image Inpainting Algorithms." CVR Journal of Science & Technology 7, no. 1 (2014): 48–52. http://dx.doi.org/10.32377/cvrjst0708.

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Dissertations / Theses on the topic "Inpainting Algorithms"

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Mahalingam, Vijay Venkatesh. "DIGITAL INPAINTING ALGORITHMS AND EVALUATION." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_diss/55.

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Digital inpainting is the technique of filling in the missing regions of an image or a video using information from surrounding area. This technique has found widespread use in applications such as restoration, error recovery, multimedia editing, and video privacy protection. This dissertation addresses three significant challenges associated with the existing and emerging inpainting algorithms and applications. The three key areas of impact are 1) Structure completion for image inpainting algorithms, 2) Fast and efficient object based video inpainting framework and 3) Perceptual evaluation of
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Li, Sheng. "An Evaluation of Image Inpainting Algorithms." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-15045.

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Image inpainting is a technique to repair damaged images or to remove/replace select regions. It was used to repair old artwork. It is also a part of movie special effects. With the extended application of electronic products, a lot of work done on mobile devices. But many algorithms may be unsuitable for mobile devices. The purpose of this work is to present an evaluation of several algorithms of image inpainting created in the past. Two classes of algorithms: Partial Differential Equations (PDEs) based algorithms and Exemplar-Based algorithms. The results show the advantages and disadvantage
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Hocking, Laird Robert. "Shell-based geometric image and video inpainting." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/281805.

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The subject of this thesis is a class of fast inpainting methods (image or video) based on the idea of filling the inpainting domain in successive shells from its boundary inwards. Image pixels (or video voxels) are filled by assigning them a color equal to a weighted average of either their already filled neighbors (the ``direct'' form of the method) or those neighbors plus additional neighbors within the current shell (the ``semi-implicit'' form). In the direct form, pixels (voxels) in the current shell may be filled independently, but in the semi-implicit form they are filled simultaneously
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Esser, John Ernest. "Primal dual algorithms for convex models and applications to image restoration, registration and nonlocal inpainting." Diss., Restricted to subscribing institutions, 2010. http://proquest.umi.com/pqdweb?did=2023768061&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Köhler, Rolf [Verfasser], and Hendrik [Akademischer Betreuer] Lensch. "Advances in computational imaging : Benchmarking Deblurring Algorithms, Deep Neural Inpainting, Depth Estimation from Light Fields / Rolf Köhler ; Betreuer: Hendrik Lensch." Tübingen : Universitätsbibliothek Tübingen, 2016. http://d-nb.info/1164170279/34.

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Mach, Václav. "Doplňování chybějících vzorků v audio signálu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-240542.

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V oblasti zpracování signálů se v současné době čím dál více využívají tzv. řídké reprezentace signálů, tzn. že daný signál je možné vyjádřit přesně či velmi dobře aproximovat lineární kombinací velmi malého počtu vektorů ze zvoleného reprezentačního systému. Tato práce se zabývá využitím řídkých reprezentací pro rekonstrukci poškozených zvukových záznamů, ať už historických nebo nově vzniklých. Především historické zvukové nahrávky trpí zarušením jako praskání nebo šum. Krátkodobé poškození zvukových nahrávek bylo doposud řešeno interpolačními technikami, zejména pomocí autoregresního modelov
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Bartlová, Hana. "Doplňování chybějících dat ve zvukových signálech." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-232075.

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This thesis deals with audio inpainting problem. Firstly, basic concepts are summarized. Then, sparse representation of signals is introduced along with several algorithms. In the main part dedicated to the audio inpainting, the problem is defined and actual methods are presented and compared. The newest approach using the harmonic strucure of sound signals is then introduced, followed by several experiments and evaluation. Lastly, an algorithm ensuring the maximal computational efficiency is derived.
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Paredes, Zevallos Daniel Leoncio. "Multi-scale image inpainting with label selection based on local statistics." Master's thesis, Pontificia Universidad Católica del Perú, 2014. http://tesis.pucp.edu.pe/repositorio/handle/123456789/5578.

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We proposed a novel inpainting method where we use a multi-scale approach to speed up the well-known Markov Random Field (MRF) based inpainting method. MRF based inpainting methods are slow when compared with other exemplar-based methods, because its computational complexity is O(jLj2) (L feasible solutions’ labels). Our multi-scale approach seeks to reduces the number of the L (feasible) labels by an appropiate selection of the labels using the information of the previous (low resolution) scale. For the initial label selection we use local statistics; moreover, to compensate the loss of infor
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Gambuti, Serena. "Fotoritocco di immagini tramite algoritmi con la tecnica dell'inpainting." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8894/.

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Kolbábková, Anežka. "Algoritmy doplňování chybějících dat v audiosignálech." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231131.

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Tato práce se zabývá doplňováním chybějících dat do audio signálů a algoritmy řešícími problém založenými na řídké reprezentaci audio signálu. Práce se zaměřuje na některé algoritmy, které řeší doplňování chybějících dat do audio signálů pomocí řídké reprezentace signálů. Součástí práce je také návrh algoritmu, který používá řídkou reprezentaci signálu a také nízkou hodnost signálu ve spektrogramu audio signálu. Dále práce uvádí implementaci tohoto algoritmu v programu Matlab a jeho vyhodnocení.
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Book chapters on the topic "Inpainting Algorithms"

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Gu, Shuhang, and Radu Timofte. "A Brief Review of Image Denoising Algorithms and Beyond." In Inpainting and Denoising Challenges. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25614-2_1.

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Fritzsche, Willy, Steffen Goebbels, Simon Hensel, Marco Rußinski, and Nils Schuch. "Inpainting Applied to Facade Images: A Comparison of Algorithms." In Pattern Recognition and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09037-0_34.

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Ballester, Coloma, Aurélie Bugeau, Samuel Hurault, Simone Parisotto, and Patricia Vitoria. "An Analysis of Generative Methods for Multiple-Image Inpainting." In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-03009-4_119-1.

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Ballester, Coloma, Aurélie Bugeau, Samuel Hurault, Simone Parisotto, and Patricia Vitoria. "An Analysis of Generative Methods for Multiple Image Inpainting." In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-98661-2_119.

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Hahn, Jooyoung, Ginmo J. Chung, Yu Wang, and Xue-Cheng Tai. "Fast Algorithms for p-elastica Energy with the Application to Image Inpainting and Curve Reconstruction." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24785-9_15.

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Voronin, Viacheslav, Vladimir Marchuk, Dmitriy Bezuglov, and Maria Butakova. "Inpainting Strategies for Reconstruction of Missing Data in Images and Videos: Techniques, Algorithms and Quality Assessment." In Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16). Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33816-3_17.

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Chede, Mayuri D., and Shilpa P. Metkar. "Inpainting Algorithm for Video Processing." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7386-1_60.

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Ahipo, Y., D. Auroux, L. D. Cohen, and M. Masmoudi. "A Hybrid Scheme for Contour Detection and Completion Based on Topological Gradient and Fast Marching Algorithms - Application to Inpainting and Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24785-9_33.

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Jurio, Aranzazu, Daniel Paternain, Miguel Pagola, Cedric Marco-Detchart, and Humberto Bustince. "Two-step Algorithm for Image Inpainting." In Advances in Fuzzy Logic and Technology 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66824-6_27.

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Ghorai, Mrinmoy, Sekhar Mandal, and Bhabatosh Chanda. "A Two-Step Image Inpainting Algorithm Using Tensor SVD." In Computer Vision - ACCV 2014 Workshops. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16631-5_5.

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Conference papers on the topic "Inpainting Algorithms"

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Shih, T. K., and Rong-Chi Chang. "Digital inpainting - survey and multilayer image inpainting algorithms." In Proceedings. Third International Conference on Information Technology and Applications. IEEE, 2005. http://dx.doi.org/10.1109/icita.2005.169.

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Patil, Balasaheb H., and Predeep M. Patil. "Comparative Analysis of Inpainting Algorithms." In 2018 International Conference on Advances in Communication and Computing Technology (ICACCT). IEEE, 2018. http://dx.doi.org/10.1109/icacct.2018.8529576.

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Молодецких, Иван, Ivan Molodetskikh, Михаил Ерофеев, Mikhail Erofeev, Дмитрий Ватолин, and Dmitriy Vatolin. "Perceptually Motivated Method for Image Inpainting Comparison." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-131-135.

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The field of automatic image inpainting has progressed rapidly in recent years, but no one has yet proposed a standard method of evaluating algorithms. This absence is due to the problem’s challenging nature: image-­inpainting algorithms strive for realism in the resulting images, but realism is a subjective concept intrinsic to human perception. Existing objective image-­quality metrics provide a poor approximation of what humans consider more or less realistic. 
 To improve the situation and to better organize both prior and future research in this field, we conducted a subjective compa
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He, Yuqing, Zhengxin Hou, and Chengyou Wang. "Two fast algorithms of image inpainting." In Photonics Asia 2007, edited by Liwei Zhou, Chung-Sheng Li, and Minerva M. Yeung. SPIE, 2007. http://dx.doi.org/10.1117/12.757191.

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Ciobanu, Sebastian, and Liviu Ciortuz. "Semantic Image Inpainting via Maximum Likelihood." In 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2020. http://dx.doi.org/10.1109/synasc51798.2020.00034.

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Wang, Liang, and Gang Wu. "GPU CUDA accelerated video inpainting using synthetic algorithms." In 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2017. http://dx.doi.org/10.1109/iccwamtip.2017.8301466.

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Yu, Yibin, Jinguo Cao, Yaofang Tang, and JunYing Gan. "Fast Wavelet Thresholding Algorithms for Face Image Inpainting." In 2013 IEEE International Conference on Green Computing and Communications (GreenCom) and IEEE Internet of Things(iThings) and IEEE Cyber, Physical and Social Computing(CPSCom). IEEE, 2013. http://dx.doi.org/10.1109/greencom-ithings-cpscom.2013.254.

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King, Emily J., Gitta Kutyniok, and Wang-Q. Lim. "Image inpainting: theoretical analysis and comparison of algorithms." In SPIE Optical Engineering + Applications, edited by Dimitri Van De Ville, Vivek K. Goyal, and Manos Papadakis. SPIE, 2013. http://dx.doi.org/10.1117/12.2025401.

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Artinescu, Irina Maria, and Costin Radu Boldea. "An Image Inpainting Technique Based on Parallel Projection Methods." In 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2018. http://dx.doi.org/10.1109/synasc.2018.00027.

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Mafteiu-Scai, Liviu, Irina Maria Artinescu, and Vlad Mafteiu-Scai. "Parallelizations of an Inpainting Algorithm Based on Convex Feasibility." In 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2018. http://dx.doi.org/10.1109/synasc.2018.00062.

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