The degree of difficulty in image inpainting depends on the types and sizes of the missing parts. Existing image inpainting approaches usually encounter difficulties in completing the missing parts in the wild with pleasing visual and contextual results as they are trained for either dealing with one specific type of missing patterns (mask) or unilaterally assuming the shapes and/or sizes of the masked areas. We propose a deep generative inpainting network, named DeepGIN, to handle various types of masked images. We design a Spatial Pyramid Dilation (SPD) ResNet block to enable the use of distant features for reconstruction. We also employ Multi-Scale Self-Attention (MSSA) mechanism and Back Projection (BP) technique to enhance our inpainting results. Our DeepGIN outperforms the state-of-the-art approaches generally, including two publicly available datasets (FFHQ and Oxford Buildings), both quantitatively and qualitatively. We also demonstrate that our model is capable of completing masked images in the wild.
Recovering badly damaged face images is a useful yet challenging task, especially in extreme cases where the masked or damaged region is very large. One of the major challenges is the ability of the system to generalize on faces outside the training dataset. We propose to tackle this extreme inpainting task with a conditional Generative Adversarial Network (GAN) that utilizes structural information, such as edges, as a prior condition. Edge information can be obtained from the partially masked image and a structurally similar image or a hand drawing. In our proposed conditional GAN, we pass th…
Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pret…
Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives. However, on the one hand, building a state-of-the-art deep inpainting model is an extremely complex task, and on the other hand, the resulting performance gains are sometimes very limited. We believe that besides the frameworks of inpainting models, lightweight traditional image processing techniques, which are often overlooked, can actually be helpful to these deep models. In this paper, we enhance the deep image inpainting models with the help of class…
Reference-guided image inpainting restores image pixels by leveraging the content from another single reference image. The primary challenge is how to precisely place the pixels from the reference image into the hole region. Therefore, understanding the 3D geometry that relates pixels between two views is a crucial step towards building a better model. Given the complexity of handling various types of reference images, we focus on the scenario where the images are captured by freely moving the same camera around. Compared to the previous work, we propose a principled approach that does not mak…
Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literature, it is challenging to directly apply the state-of-the-art inpainting methods to image extension as they tend to generate blurry or repetitive pixels with inconsistent semantics. We introduce semantic conditioning to the discriminator of a generative adversarial network (GAN), and achieve strong results on image extension with coherent semantics and visually pleasing colors and textures. We also show promising res…
This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint considerably large part of the image using no supervision but the context. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the image to inpaint. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the …
Image reconstruction including image restoration and denoising is a challenging problem in the field of image computing. We present a new method, called X-GANs, for reconstruction of arbitrary corrupted resource based on a variant of conditional generative adversarial networks (conditional GANs). In our method, a novel generator and multi-scale discriminators are proposed, as well as the combined adversarial losses, which integrate a VGG perceptual loss, an adversarial perceptual loss, and an elaborate corresponding point loss together based on the analysis of image feature. Our conditional GA…
Centre for Research and Technology Hellas（希腊研究与技术中心）
The task of transforming a furnished room image into a background-only is extremely challenging since it requires making large changes regarding the scene context while still preserving the overall layout and style. In order to acquire photo-realistic and structural consistent background, existing deep learning methods either employ image inpainting approaches or incorporate the learning of the scene layout as an individual task and leverage it later in a not fully differentiable semantic region-adaptive normalization module. To tackle these drawbacks, we treat scene layout generation as a fea…
Instituto de Telecomunicações（电信研究院）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）
This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics. In a series of experiments with simulated and real data, the newly introduce…
Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image inpainting or autonomous vehicles and robots. While imputation-based techniques are still one of the most popular solutions, they frequently introduce unreliable information to the data and do not take into account the uncertainty of estimation, which may be destructive for a machine learning model. In this paper, we present MisConv, a general mechanism, for adapting various CNN architectures to process incomplete i…
Beijing University of Posts and Telecommunications（北京邮电大学）
Low-dose Computed Tomography is a common issue in reality. Current reduction, sparse sampling and limited-view scanning can all cause it. Between them, limited-view CT is general in the industry due to inevitable mechanical and physical limitation. However, limited-view CT can cause serious imaging problem on account of its massive information loss. Thus, we should effectively utilize the scant prior information to perform completion. It is an undeniable fact that CT imaging slices are extremely dense, which leads to high continuity between successive images. We realized that fully exploit the…
High-Fidelity Synthetic Transmission Electron Microscopy Image Generation Using Diffusion Probabilistic Models for Data-Limited Semiconductor Metrology
Chemnitz University of Technology, Chemnitz, Germany（化学工业大学，化学矿泉，德国）；Interuniversity Microelectronics Centre (imec), Leuven, Belgium（大学微电子中心（imec），卢汶，比利时）
School of Computer；Communication Sciences, EPFL, Switzerland
Extreme image or video completion, where, for instance, we only retain 1% of pixels in random locations, allows for very cheap sampling in terms of the required pre-processing. The consequence is, however, a reconstruction that is challenging for humans and inpainting algorithms alike. We propose an extension of a state-of-the-art extreme image completion algorithm to extreme video completion. We analyze a color-motion estimation approach based on color KL-divergence that is suitable for extremely sparse scenarios. Our algorithm leverages the estimate to adapt between its spatial and temporal …
American International University-Bangladesh（美国国际大学-孟加拉国）
While deep learning-based image reconstruction methods have shown significant success in removing objects from pictures, they have yet to achieve acceptable results for attributing consistency to gender, ethnicity, expression, and other characteristics like the topological structure of the face. The purpose of this work is to extract the mask region from a masked image and rebuild the area that has been detected. This problem is complex because (i) it is difficult to determine the gender of an image hidden behind a mask, which causes the network to become confused and reconstruct the male face…
Euclid Quick Data Release (Q1). Active galactic nuclei identification using diffusion-based inpainting of Euclid VIS images
School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol, BS8 1TL, UK aff1 Max-Planck-Institut f\"ur Astronomie, K\"onigstuhl 17, 69117 Heidelberg, Germany aff2 SRON Netherlands Institute for Space Research, Landleven 12, 9747 AD, Groningen, The Netherlands aff3 Instituto de Astrof\' sica de Canarias, V\' a L\'actea, 38205 La Laguna, Tenerife, Spain aff4 Instituto de Astrof\'isica de Canarias (IAC)；Departamento de Astrof\'isica, Universidad de La Laguna (ULL), 38200, La Laguna, Tenerife, Spain aff5 Universit\'e PSL, Observatoire de Paris, Sorbonne Universit\'e, CNRS, LERMA, 75014, Paris, France aff6 Universit\'e Paris-Cit\'e, 5 Rue Thomas Mann, 75013, Paris, France aff7 School of Physics, Astronomy；Mathematics, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK aff8 Aspia Space, Falmouth, TR10 9TA, UK aff9 David A. Dunlap Department of Astronomy \& Astrophysics, University of Toronto, 50 St George Street, Toronto, Ontario M5S 3H4, Canada aff10 Jodrell Bank Centre for Astrophysics, Department of Physics；Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK aff11 Max Planck Institute for Extraterrestrial Physics, Giessenbachstr. 1, 85748 Garching, Germany aff12 Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain aff13 Institut d'Estudis Espacials de Catalunya (IEEC), Edifici RDIT, Campus UPC, 08860 Castelldefels, Barcelona, Spain aff14 Centro de Astrof\' sica da Universidade do Porto, Rua das Estrelas, 4150-762 Porto, Portugal aff15 Instituto de Astrof\'isica e Ci\ encias do Espa c o, Universidade do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal aff16 Dipartimento di Fisica e Astronomia "Augusto Righi" - Alma Mater Studiorum Universit\`a di Bologna, via Piero Gobetti 93/2, 40129 Bologna, Italy aff17 INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Via Piero Gobetti 93/3, 40129 Bologna, Italy aff18 Department of Mathematics；Physics, Roma Tre University, Via della Vasca Navale 84, 00146 Rome, Italy aff19 INAF-Osservatorio Astronomico di Roma, Via Frascati 33, 00078 Monteporzio Catone, Italy aff20 Universit\'e Paris-Saclay, CNRS, Institut d'astrophysique spatiale, 91405, Orsay, France aff21 ESAC/ESA, Camino Bajo del Castillo, s/n., Urb. Villafranca del Castillo, 28692 Villanueva de la Ca\ nada, Madrid, Spain aff22 INAF-Osservatorio Astronomico di Brera, Via Brera 28, 20122 Milano, Italy aff23 Universit\'e Paris-Saclay, Universit\'e Paris Cit\'e, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France aff24 IFPU, Institute for Fundamental Physics of the Universe, via Beirut 2, 34151 Trieste, Italy aff25 INAF-Osservatorio Astronomico di Trieste, Via G. B. 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Pancini", University Federico II, Via Cinthia 6, 80126, Napoli, Italy aff34 INAF-Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131 Napoli, Italy aff35 Faculdade de Ci\ encias da Universidade do Porto, Rua do Campo de Alegre, 4150-007 Porto, Portugal aff36 Dipartimento di Fisica, Universit\`a degli Studi di Torino, Via P. Giuria 1, 10125 Torino, Italy aff37 INFN-Sezione di Torino, Via P. Giuria 1, 10125 Torino, Italy aff38 INAF-Osservatorio Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese (TO), Italy aff39 European Space Agency/ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands aff40 Institute Lorentz, Leiden University, Niels Bohrweg 2, 2333 CA Leiden, The Netherlands aff41 Leiden Observatory, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands aff42 INAF-IASF Milano, Via Alfonso Corti 12, 20133 Milano, Italy aff43 Centro de Investigaciones Energ\'eticas, Medioambientales y Tecnol\'ogicas (CIEMAT), Avenida Complutense 40, 28040 Madrid, Spain aff44 Port d'Informaci\' o Cient\' i fica, Campus UAB, C. Albareda s/n, 08193 Bellaterra (Barcelona), Spain aff45 INFN section of Naples, Via Cinthia 6, 80126, Napoli, Italy aff46 Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA aff47 Dipartimento di Fisica e Astronomia "Augusto Righi" - Alma Mater Studiorum Universit\`a di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy aff48 Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK aff49 European Space Agency/ESRIN, Largo Galileo Galilei 1, 00044 Frascati, Roma, Italy aff50 Universit\'e Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, UMR 5822, Villeurbanne, F-69100, France aff51 Aix-Marseille Universit\'e, CNRS, CNES, LAM, Marseille, France aff52 Institut de Ci\` e ncies del Cosmos (ICCUB), Universitat de Barcelona (IEEC-UB), Mart\' i i Franqu\` e s 1, 08028 Barcelona, Spain aff53 Instituci\'o Catalana de Recerca i Estudis Avan c ats (ICREA), Passeig de Llu\' s Companys 23, 08010 Barcelona, Spain aff54 UCB Lyon 1, CNRS/IN2P3, IUF, IP2I Lyon, 4 rue Enrico Fermi, 69622 Villeurbanne, France aff55 Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, Surrey RH5 6NT, UK aff56 Departamento de F\'isica, Faculdade de Ci\ encias, Universidade de Lisboa, Edif\'icio C8, Campo Grande, PT1749-016 Lisboa, Portugal aff57 Instituto de Astrof\'isica e Ci\ encias do Espa c o, Faculdade de Ci\ encias, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal aff58 Department of Astronomy, University of Geneva, ch. d'Ecogia 16, 1290 Versoix, Switzerland aff59 INFN-Padova, Via Marzolo 8, 35131 Padova, Italy aff60 Aix-Marseille Universit\'e, CNRS/IN2P3, CPPM, Marseille, France aff61 INAF-Istituto di Astrofisica e Planetologia Spaziali, via del Fosso del Cavaliere, 100, 00100 Roma, Italy aff62 Universit\"ats-Sternwarte M\"unchen, Fakult\"at f\"ur Physik, Ludwig-Maximilians-Universit\"at M\"unchen, Scheinerstrasse 1, 81679 M\"unchen, Germany aff63 INAF-Osservatorio Astronomico di Padova, Via dell'Osservatorio 5, 35122 Padova, Italy aff64 Institute of Theoretical Astrophysics, University of Oslo, P.O. 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Marconi, 00185 Roma, Italy aff82 Department of Physics, Institute for Computational Cosmology, Durham University, South Road, Durham, DH1 3LE, UK aff83 Universit\'e C\ o te d'Azur, Observatoire de la C\ o te d'Azur, CNRS, Laboratoire Lagrange, Bd de l'Observatoire, CS 34229, 06304 Nice cedex 4, France aff84 Universit\'e Paris Cit\'e, CNRS, Astroparticule et Cosmologie, 75013 Paris, France aff85 CNRS-UCB International Research Laboratory, Centre Pierre Binetruy, IRL2007, CPB-IN2P3, Berkeley, USA aff86 University of Applied Sciences；Arts of Northwestern Switzerland, School of Engineering, 5210 Windisch, Switzerland aff87 Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique F\'ed\'erale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland aff88 Aurora Technology for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Ca\ nada, 28692 Madrid, Spain aff89 Institut de F\' i sica d'Altes Energies (IFAE), The Barcelona Institute of Science；Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain aff90 DARK, Niels Bohr Institute, University of Copenhagen, Jagtvej 155, 2200 Copenhagen, Denmark aff91 Waterloo Centre for Astrophysics, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada aff92 Department of Physics；Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada aff93 Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada aff94 Centre National d'Etudes Spatiales -- Centre spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France aff95 Institute of Space Science, Str. 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Galilei", Universit\`a di Padova, Via Marzolo 8, 35131 Padova, Italy aff99 Departamento de F\'isica, FCFM, Universidad de Chile, Blanco Encalada 2008, Santiago, Chile aff100 Universit\"at Innsbruck, Institut f\"ur Astro- und Teilchenphysik, Technikerstr. 25/8, 6020 Innsbruck, Austria aff101 Satlantis, University Science Park, Sede Bld 48940, Leioa-Bilbao, Spain aff102 Centre for Electronic Imaging, Open University, Walton Hall, Milton Keynes, MK7 6AA, UK aff103 Instituto de Astrof\'isica e Ci\ encias do Espa c o, Faculdade de Ci\ encias, Universidade de Lisboa, Tapada da Ajuda, 1349-018 Lisboa, Portugal aff104 Cosmic Dawn Center (DAWN) aff105 Niels Bohr Institute, University of Copenhagen, Jagtvej 128, 2200 Copenhagen, Denmark aff106 Universidad Polit\'ecnica de Cartagena, Departamento de Electr\'onica y Tecnolog\'ia de Computadoras, Plaza del Hospital 1, 30202 Cartagena, Spain aff107 Institut de Recherche en Astrophysique et Plan\'etologie (IRAP), Universit\'e de Toulouse, CNRS, UPS, CNES, 14 Av. 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Universidad de Salamanca. Plaza de la Merced s/n. 37008 Salamanca, Spain aff125 Universit\'e de Strasbourg, CNRS, Observatoire astronomique de Strasbourg, UMR 7550, 67000 Strasbourg, France aff126 Center for Data-Driven Discovery, Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan aff127 Dipartimento di Fisica - Sezione di Astronomia, Universit\`a di Trieste, Via Tiepolo 11, 34131 Trieste, Italy aff128 ICSC - Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing, Via Magnanelli 2, Bologna, Italy aff129 California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA aff130 Departamento F\'isica Aplicada, Universidad Polit\'ecnica de Cartagena, Campus Muralla del Mar, 30202 Cartagena, Murcia, Spain aff131 Instituto de F\'isica de Cantabria, Edificio Juan Jord\'a, Avenida de los Castros, 39005 Santander, Spain aff132 INFN, Sezione di Lecce, Via per Arnesano, CP-193, 73100, Lecce, Italy aff133 Department of Mathematics；Physics E. 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Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 53, Avenue des Martyrs, 38000, Grenoble, France aff152 INAF-Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125, Firenze, Italy aff153 Dipartimento di Fisica, Sapienza Universit\`a di Roma, Piazzale Aldo Moro 2, 00185 Roma, Italy aff154 HE Space for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Ca\ nada, 28692 Madrid, Spain aff155 Theoretical astrophysics, Department of Physics；Astronomy, Uppsala University, Box 515, 751 20 Uppsala, Sweden aff156 Mathematical Institute, University of Leiden, Einsteinweg 55, 2333 CA Leiden, The Netherlands aff157 School of Physics \& Astronomy, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK aff158 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK aff159 Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544, USA aff160 Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, 10010, New York, NY, USA aff161
Out-of-distribution (OOD) detection can be used in deep learning-based applications to reject outlier samples from being unreliably classified by deep neural networks. Learning to classify between OOD and in-distribution samples is difficult because data comprising the former is extremely diverse. It has been observed that an auxiliary OOD dataset is most effective in training a "rejection" network when its samples are semantically similar to in-distribution images. We first deduce that OOD images are perceived by a deep neural network to be semantically similar to in-distribution samples when…
