Relevant bibliographies by topics / Image inpainting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image inpainting'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Image inpainting"

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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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Wang, Zhuowei, Xiaoqi Jiang, Chong Chen, and Yanxi Li. "Lightweight Multi-Scales Feature Diffusion for Image Inpainting Towards Underwater Fish Monitoring." Journal of Marine Science and Engineering 12, no. 12 (2024): 2178. http://dx.doi.org/10.3390/jmse12122178.

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In the process of gradually upgrading aquaculture to the information and intelligence industries, it is usually necessary to collect images of underwater fish. In practical work, the quality of underwater images is often affected by water clarity and light refraction, resulting in most fish images not fully displaying the entire fish body. Image inpainting helps infer the occluded fish image information based on known images, thereby better identifying and analyzing fish populations. When using existing image inpainting methods for underwater fish images, limited by the small datasets availabl
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Xiao, Lifang, and Jianhao Wu. "Image inpainting algorithm based on double curvature-driven diffusion model with P-Laplace operator." PLOS ONE 19, no. 7 (2024): e0305470. http://dx.doi.org/10.1371/journal.pone.0305470.

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The method of partial differential equations for image inpainting achieves better repair results and is economically feasible with fast repair time. Addresses the inability of Curvature-Driven Diffusion (CDD) models to repair complex textures or edges when the input image is affected by severe noise or distortion, resulting in discontinuous repair features, blurred detail textures, and an inability to deal with the consistency of global image content, In this paper, we have the CDD model of P-Laplace operator term to image inpainting. In this method, the P-Laplace operator is firstly introduce
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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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Cannas, Edoardo Daniele, Sara Mandelli, Paolo Bestagini, Stefano Tubaro, and Edward J. Delp. "Deep Image Prior Amplitude SAR Image Anonymization." Remote Sensing 15, no. 15 (2023): 3750. http://dx.doi.org/10.3390/rs15153750.

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This paper presents an extensive evaluation of the Deep Image Prior (DIP) technique for image inpainting on Synthetic Aperture Radar (SAR) images. SAR images are gaining popularity in various applications, but there may be a need to conceal certain regions of them. Image inpainting provides a solution for this. However, not all inpainting techniques are designed to work on SAR images. Some are intended for use on photographs, while others have to be specifically trained on top of a huge set of images. In this work, we evaluate the performance of the DIP technique that is capable of addressing
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Suma N and Dr. Kusuma Kumari B.M. "Understanding Image Inpainting." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 02 (2025): 319–22. https://doi.org/10.47392/irjaem.2025.0050.

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Image inpainting is a computer vision technique that aims to fill in missing or damaged areas of an image with plausible content. The goal is to create a visually coherent and realistic image that seamlessly integrates the inpainted regions with the surrounding context. Image inpainting techniques typically utilize nearby information or context to infer the missing or damaged pixels. They can be broadly classified into two categories: exemplar-based inpainting methods and partial differential equation-based inpainting methods. Exemplar-based inpainting methods rely on the idea of finding simil
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Prasanthi, Tammineni Shanmukha, Swarajya Madhuri Rayavarapu, Yenneti Laxmi Lavanya, Gottapu Santosh Kumar, and Gottapu Sasibhushana Rao. "Investigation of Optimal Image Inpainting Techniques for Image Reconstruction and Image Restoration Applications." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6s (2023): 271–76. http://dx.doi.org/10.17762/ijritcc.v11i6s.6918.

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People in today's society take a lot of pictures with their smartphones and also make an effort to keep their old photographs safe, but with time, those photographs deteriorate. Image inpainting is the art of reconstructing damaged or missing parts of an image. Repairing scratches in photographs or film negatives, or adding or removing elements like stamped dates or "red-eye," are all possible through inpainting. In order to restore the image many techniques have been developed, significant techniques include exemplar based inpainting, coherent based inpainting and method for correction of non
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Charef, Ayoub, and Ahmed Ouqour. "Improving Image Inpainting through Contextual Attention in Deep Learning." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 14904–9. http://dx.doi.org/10.48084/etasr.7347.

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Image processing is vital in modern technology, offering a diverse range of techniques for manipulating digital images to extract valuable information or enhance visual quality. Among these techniques, image inpainting stands out, involving the reconstruction or restoration of missing or damaged regions within images. This study explores advances in image inpainting and presents a novel approach that integrates coarse-to-fine inpainting and attention-based inpainting techniques. The proposed method leverages deep learning methods to enhance the quality and efficiency of image inpainting, achie
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Dissertations / Theses on the topic "Image inpainting"

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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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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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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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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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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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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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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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Carola-Bibiane Schönlieb. Partial Differential Equation Methods for Image Inpainting. Cambridge University Press, 2015.

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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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März, Thomas. First Order Quasi-Linear PDEs with BV Boundary Data and Applications to Image Inpainting. Logos Verlag Berlin, 2010.

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Book chapters on the topic "Image inpainting"

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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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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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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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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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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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Patil, Soureesh, Amit Joshi, and Suraj Sawant. "Recovering Images Using Image Inpainting Techniques." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0236-1_3.

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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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Conference papers on the topic "Image inpainting"

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Min, Xiqing, Wenchao Zheng, and Yuexiu Xing. "Simplified Fast Image Inpainting Method." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865397.

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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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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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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. &#x0D; 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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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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Li, Ang, Shanshan Zhao, Zhang Qingjie, and Qiuhong Ke. "Iterative Geometry-Aware Cross Guidance Network for Stereo Image Inpainting." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/147.

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Currently, single image inpainting has achieved promising results based on deep convolutional neural networks. However, inpainting on stereo images with missing regions has not been explored thoroughly, which is also a significant but different problem. One crucial requirement for stereo image inpainting is stereo consistency. To achieve it, we propose an Iterative Geometry-Aware Cross Guidance Network (IGGNet). The IGGNet contains two key ingredients, i.e., a Geometry-Aware Attention(GAA) module and an Iterative Cross Guidance(ICG) strategy. The GAA module relies on the epipolar geometry cues
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Huang, Muqi, Chaoyue Wang, Yong Luo, and Lefei Zhang. "Eliminating the Cross-Domain Misalignment in Text-guided Image Inpainting." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/97.

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Text-guided image inpainting has rapidly garnered prominence as a task in user-directed image synthesis, aiming to complete the occluded image regions following the textual prompt provided. However, current methods usually grapple with issues arising from the disparity between low-level pixel data and high-level semantic descriptions, which results in inpainted sections not harmonizing with the original image (either structurally or texturally). In this study, we introduce a Structure-Aware Inpainting Learning scheme and an Asymmetric Cross Domain Attention to address these cross-domain misali
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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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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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