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Relevant bibliographies by topics / Image inpainting

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Academic literature on the topic 'Image inpainting'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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Journal articles on the topic "Image inpainting"

1

Jia, Zhaoyang, and Guangxue Chen. "Study on Digital Image Inpainting Method Based on Multispectral Image Decomposition Synthesis." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 01 (2018): 1954004. http://dx.doi.org/10.1142/s0218001419540041.

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The paper analyzes the image inpainting problem of damaged Painting Arts for high fidelity images reproduction, and a digital image inpainting method based on multispectral image decomposition synthesis is proposed. Firstly, multi-channel images of Painting Arts are obtained by multispectral technology. Then, a polynomial regression method based on principal component is used to reconstruct the spectral image. The reconstructed image is decomposed by VO image decomposition model. During the inpainting process, the channel correlation of the structure image and the texture image of multispectra

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Wang, Fang, and Mei Hua Xie. "Image Inpainting Based on Compressed Sensing." Advanced Materials Research 317-319 (August 2011): 2254–57. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.2254.

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An inpainting method is proposed to restore the image containing some missing patches. Based on the spartity of images, the method translates the problem of image inpainting into image reconstruction in compressed sensing. The inpainting model is solved using the split iteration. Simulated image inpainting results demonstrate the efficiency of the proposed method.

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Wang, Wenzhen, Na Deng, Binjie Xin, Chi-wai Kan, Yiliang Wang, and Shuaigang Lu. "Research on texture image inpainting of jacquard fabric based on non-single vision." Textile Research Journal 90, no. 13-14 (2019): 1462–76. http://dx.doi.org/10.1177/0040517519892924.

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For the texture image inpainting of jacquard fabric with a large damaged region but complete data information around it, traditional exemplar-based image inpainting algorithms are not only have rapid falling traditional confidence value, but also the matching precision is greatly limited due to the lack of robustness, which results in a wrong guided direction and an unsatisfactory inpainting effect. To solve the above-mentioned problems, a novel digital image acquisition system for damaged fabric was designed and one set of texture image inpainting algorithms of jacquard fabric based on non-si

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Takahashi, Tomohiro. "Image Inpainting." Journal of The Institute of Image Information and Television Engineers 71, no. 7 (2017): 503–4. http://dx.doi.org/10.3169/itej.71.503.

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Niu, Zuodong, Handong Li, Yao Li, Yingjie Mei, and Jing Yang. "An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry." Symmetry 12, no. 2 (2020): 190. http://dx.doi.org/10.3390/sym12020190.

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Face image inpainting technology is an important research direction in image restoration. When the current image restoration methods repair the damaged areas of face images with weak texture, there are problems such as low accuracy of face image decomposition, unreasonable restoration structure, and degradation of image quality after inpainting. Therefore, this paper proposes an adaptive face image inpainting algorithm based on feature symmetry. Firstly, we locate the feature points of the face, and segment the face into four feature parts based on the feature point distribution to define the

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ZHI, ZHANJIANG, and YI SUN. "AN IMAGE INPAINTING ALGORITHM BASED ON ENERGY MINIMIZATION." International Journal of Image and Graphics 12, no. 03 (2012): 1250017. http://dx.doi.org/10.1142/s0219467812500179.

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Image inpainting is an active research area in the image processing field. The essential idea of image inpainting algorithm is to fill in the missing or damaged regions with available information from their surroundings. In this paper, we propose two image inpainting models based on the variational method. We show that the diffusion performance of the proposed models for image inpainting are superior to classical total variation (TV) inpainting model according to the physical characteristics in local coordinates. To show the effective performance of the proposed models, we apply it to restorin

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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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Kim, Joohyung, Janghun Hyeon, and Nakju Doh. "Generative multiview inpainting for object removal in large indoor spaces." International Journal of Advanced Robotic Systems 18, no. 2 (2021): 172988142199654. http://dx.doi.org/10.1177/1729881421996544.

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As interest in image-based rendering increases, the need for multiview inpainting is emerging. Despite of rapid progresses in single-image inpainting based on deep learning approaches, they have no constraint in obtaining color consistency over multiple inpainted images. We target object removal in large-scale indoor spaces and propose a novel pipeline of multiview inpainting to achieve color consistency and boundary consistency in multiple images. The first step of the pipeline is to create color prior information on masks by coloring point clouds from multiple images and projecting the color

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Sun, Tingzhu, Weidong Fang, Wei Chen, Yanxin Yao, Fangming Bi, and Baolei Wu. "High-Resolution Image Inpainting Based on Multi-Scale Neural Network." Electronics 8, no. 11 (2019): 1370. http://dx.doi.org/10.3390/electronics8111370.

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Although image inpainting based on the generated adversarial network (GAN) has made great breakthroughs in accuracy and speed in recent years, they can only process low-resolution images because of memory limitations and difficulty in training. For high-resolution images, the inpainted regions become blurred and the unpleasant boundaries become visible. Based on the current advanced image generation network, we proposed a novel high-resolution image inpainting method based on multi-scale neural network. This method is a two-stage network including content reconstruction and texture detail rest

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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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Dissertations / Theses on the topic "Image inpainting"

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Runge, Joshua (Joshua Michael). "Reducing spectral reflections through image inpainting." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53120.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Includes bibliographical references (p. 45-46).<br>The goal of this thesis is to produce an image processing tool that could, with limited user interaction, significantly reduce spectral reflections. The resulting process can then be used by biological researchers in the process of cataloging and recognizing individual organisms via collected images. The process involves the generation of a mask, refinements to that mask such as background segmentation and morphological dilat

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Schönlieb, Carola-Bibiane. "Modern PDE techniques for image inpainting." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611552.

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Li, Zhi. "Variational image segmentation, inpainting and denoising." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/292.

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Variational methods have attracted much attention in the past decade. With rigorous mathematical analysis and computational methods, variational minimization models can handle many practical problems arising in image processing, such as image segmentation and image restoration. We propose a two-stage image segmentation approach for color images, in the first stage, the primal-dual algorithm is applied to efficiently solve the proposed minimization problem for a smoothed image solution without irrelevant and trivial information, then in the second stage, we adopt the hillclimbing procedure to s

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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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Ignácio, Ubiratã Azevedo. "Aplicação de wavelets em inpainting digital." Universidade do Vale do Rio do Sinos, 2007. http://www.repositorio.jesuita.org.br/handle/UNISINOS/2245.

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Made available in DSpace on 2015-03-05T13:58:27Z (GMT). No. of bitstreams: 0 Previous issue date: 26<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Inpainting Digital é uma técnica recente que permite completar a falta de informação em imagens, seja por falha ou por remoção intencional de alguma área ou objeto. Uma das atribuições importantes do inpainting digital é de que deve ser capaz de alterar uma imagem, de forma que não seja simples perceber que esta alteração foi feita; caracteriza uma modificação indetectável. Os métodos para determinar como esta falta de infor

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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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Li, Dong. "Extensor-based image and video interpolation and inpainting /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1422941.

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Almshaal, Rashwan M. "Sparse Signal Processing Based Image Compression and Inpainting." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4286.

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In this thesis, we investigate the application of compressive sensing and sparse signal processing techniques to image compression and inpainting problems. Considering that many signals are sparse in certain transformation domain, a natural question to ask is: can an image be represented by as few coefficients as possible? In this thesis, we propose a new model for image compression/decompression based on sparse representation. We suggest constructing an overcomplete dictionary by combining two compression matrices, the discrete cosine transform (DCT) matrix and Hadamard-Walsh transform (HWT)

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Ding, Ding, and Ding Ding. "Image Inpainting Based on Exemplars and Sparse Representation." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625897.

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Image inpainting is the process of recovering missing or deteriorated data within the digital images and videos in a plausible way. It has become an important topic in the area of image processing, which leads to the understanding of the textural and structural information within the images. Image inpainting has many different applications, such as image/video restoration, text/object removal, texture synthesis, and transmission error concealment. In recent years, many algorithms have been developed to solve the image inpainting problem, which can be roughly grouped into four categories, parti

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Moh, Heng Ping Nabil Christopher. "Segment-based image matting using inpainting to resolve ambiguities." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46013.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.<br>Includes bibliographical references (p. 209-211).<br>Image Matting and Compositing [6, 25] - the extraction of a foreground element from an image and overlaying it over a different background - are two important operations in digital image manipulation. The extraction of the foreground element and its composition over an existing background is performed using a mask known as an alpha matte, which is generated by Image Matting. The problem of Image Matting is inherently ill-po

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Books on the topic "Image inpainting"

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Schönlieb, Carola-Bibiane. Partial differential equation methods for image inpainting. Cambridge University Press, 2015.

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Digital Image Inpainting Completion Of Images With Missing Data Regions. VDM Verlag, 2008.

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Book chapters on the topic "Image inpainting"

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Gong, Shengrong, Chunping Liu, Yi Ji, Baojiang Zhong, Yonggang Li, and Husheng Dong. "Image Inpainting." In Advanced Image and Video Processing Using MATLAB. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77223-3_6.

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Herling, Jan. "Image Inpainting." In Advanced Real-Time Manipulation of Video Streams. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-05810-4_4.

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Xu, Jiajie, Yi Jiang, Junwu Zhu, and Baoqing Yang. "Generative Image Inpainting." In Studies in Computational Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56178-9_12.

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Morita, Satoru. "Three Dimensional Image Inpainting." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881223_94.

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Liu, Yang, Jinshan Pan, and Zhixun Su. "Deep Blind Image Inpainting." In Intelligence Science and Big Data Engineering. Visual Data Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36189-1_11.

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Gustavsson, David, Kim S. Pedersen, and Mads Nielsen. "Image Inpainting by Cooling and Heating." In Image Analysis. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73040-8_60.

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Patel, Vismay, and Anubha Pandey. "Generative Image Inpainting for Person Pose Generation." In Inpainting and Denoising Challenges. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25614-2_8.

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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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Li, Chu-Tak, Wan-Chi Siu, Zhi-Song Liu, Li-Wen Wang, and Daniel Pak-Kong Lun. "DeepGIN: Deep Generative Inpainting Network for Extreme Image Inpainting." In Computer Vision – ECCV 2020 Workshops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66823-5_1.

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Chen, Shen, Wei Ma, and Yue Qin. "CNN-Based Stereoscopic Image Inpainting." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34113-8_9.

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Conference papers on the topic "Image inpainting"

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Li, Ang, Jianzhong Qi, Rui Zhang, Xingjun Ma, and Kotagiri Ramamohanarao. "Generative Image Inpainting with Submanifold Alignment." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/114.

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Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and object removal. However, current GAN-based generative inpainting models do not explicitly exploit the structural or textural consistency between restored contents and their surrounding contexts. To address this limitation, we propose to enforce the alignment (or closeness) between the local data submanifolds (subspaces) around restored images and those around the original (uncorrupted) images during the learning process of GAN-based inpainting models. We exploit Loc

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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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Bertalmio, Marcelo, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester. "Image inpainting." In the 27th annual conference. ACM Press, 2000. http://dx.doi.org/10.1145/344779.344972.

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Hervieu, Alexandre, Nicolas Papadakis, Aurelie Bugeau, Pau Gargallo, and Vicent Caselles. "Stereoscopic Image Inpainting: Distinct Depth Maps and Images Inpainting." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.997.

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Wang, Lili, and Yi Wan. "Multiresolution image inpainting." In 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2012. http://dx.doi.org/10.1109/csae.2012.6272899.

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Ma, Wenjuan, Maolin Hu, and Pengyong Hu. "Image Inpainting under Single Image." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.317.

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Sapiro, G. "Inpainting the colors." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530151.

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Li, Ang, Qiuhong Ke, Xingjun Ma, et al. "Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/109.

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Deep image inpainting aims to restore damaged or missing regions in an image with realistic contents. While having a wide range of applications such as object removal and image recovery, deep inpainting techniques also have the risk of being manipulated for image forgery. A promising countermeasure against such forgeries is deep inpainting detection, which aims to locate the inpainted regions in an image. In this paper, we make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well when detecting different deep inpainting methods. To t

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Xiu-hong, Yang, and Guo Bao-long. "A Novel Non-local Image Inpainting Algorithm: Fractal-Based Image Inpainting." In 2009 Second International Workshop on Computer Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/wcse.2009.759.

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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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Reports on the topic "Image inpainting"

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Bertalmio, Marcelo, Guillermo Sapiro, Vicent Caselles, and Coloma Ballester. Image Inpainting. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada437378.

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Chan, Tony F., and Jianhong Shen. Variational Image Inpainting. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada437276.

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Arias, Pablo, Gabriele Facciolo, Vicent Caselles, and Guillermo Sapiro. A Variational Framework for Exemplar-Based Image Inpainting. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada540639.

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