Relevant bibliographies by topics / Automatic Colorization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Automatic Colorization'

Author: Grafiati
Published: 7 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Automatic Colorization"

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Aoki, Terumasa, and Van Nguyen. "Global Distribution Adjustment and Nonlinear Feature Transformation for Automatic Colorization." Advances in Multimedia 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/1504691.

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Automatic colorization is generally classified into two groups: propagation-based methods and reference-based methods. In reference-based automatic colorization methods, color image(s) are used as reference(s) to reconstruct original color of a gray target image. The most important task here is to find the best matching pairs for all pixels between reference and target images in order to transfer color information from reference to target pixels. A lot of attractive local feature-based image matching methods have already been developed for the last two decades. Unfortunately, as far as we know
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Alam khan, Sharique, and Alok Katiyar. "Automatic colorization of natural images using deep learning." YMER Digital 21, no. 05 (2022): 946–51. http://dx.doi.org/10.37896/ymer21.05/a6.

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An approach based on deep learning for automatic colorization of image with optional userguided hints. The system maps a grey-scale image, along with, user hints” (selected colors) to an output colorization with a Convolution Neural Network (CNN). Previous approaches have relied heavily on user input which results in non-real-time desaturated outputs. The network takes user edits by fusing low-level information of source with high-level information, learned from large-scale data. Some networks are trained on a large data set to eliminate this dependency. The image colorization systems find the
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Prasanna, N. Lakshmi, Sk Sohal Rehman, V. Naga Phani, S. Koteswara Rao, and T. Ram Santosh. "AUTOMATIC COLORIZATION USING CONVOLUTIONAL NEURAL NETWORKS." International Journal of Computer Science and Mobile Computing 10, no. 7 (2021): 10–19. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.002.

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Automatic Colorization helps to hallucinate what an input gray scale image would look like when colorized. Automatic coloring makes it look and feel better than Grayscale. One of the most important technologies used in Machine learning is Deep Learning. Deep learning is nothing but to train the computer with certain algorithms which imitates the working of the human brain. Some of the areas in which it is used are medical, Industrial Automation, Electronics etc. The main objective of this project is coloring Grayscale images. We have umbrellaed the concepts of convolutional neural networks alo
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Farella, Elisa Mariarosaria, Salim Malek, and Fabio Remondino. "Colorizing the Past: Deep Learning for the Automatic Colorization of Historical Aerial Images." Journal of Imaging 8, no. 10 (2022): 269. http://dx.doi.org/10.3390/jimaging8100269.

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The colorization of grayscale images can, nowadays, take advantage of recent progress and the automation of deep-learning techniques. From the media industry to medical or geospatial applications, image colorization is an attractive and investigated image processing practice, and it is also helpful for revitalizing historical photographs. After exploring some of the existing fully automatic learning methods, the article presents a new neural network architecture, Hyper-U-NET, which combines a U-NET-like architecture and HyperConnections to handle the colorization of historical black and white
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Adhithya, Raguram, I. P. Venkatesh, and S. Srividhya Dr. "Automatic Image Colorization using Generative Adversarial Networks." Advancement in Image Processing and Pattern Recognition 5, no. 2 (2022): 1–5. https://doi.org/10.5281/zenodo.6758133.

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Image colorization is an approach of transforming a black and white image into colorized image. The colonization process can also be used to perform color corrections. This application has been incorporated&nbsp; in large software like Adobe Photoshop, After Effects and Lightroom, and Da Vinci Resolve to aid users through their editing process. In the past, the process of colorization required a tremendous amount of human involvement and the results were still not properly saturated. <em>The approach considered for this topic is a fully generalized procedure using</em> <em>a conditional Deep C
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Netha, Guda Pranay, M. S. S. Manohar, M. Sai Amartya Maruth, and Ganjikunta Ganesh Kumar. "Colourization of Black and White Images using Deep Learning." International Journal of Computer Science and Mobile Computing 11, no. 1 (2022): 116–21. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.014.

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Colorization is the process of transforming grayscale photos into colour images that are aesthetically appealing. The basic objective is to persuade the spectator that the outcome is genuine. The majority of grayscale photographs that need to be colourized are of nature situations. Over the last 20 years, a broad range of colorization methods have been created, ranging from algorithmically simple but time- and energy-consuming procedures due to inescapable human participation to more difficult but also more automated ones. Automatic conversion has evolved into a difficult field that mixes mach
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Man, Qiaoyue, and Young-Im Cho. "Efficient Comic Content Extraction and Coloring Composite Networks." Applied Sciences 15, no. 5 (2025): 2641. https://doi.org/10.3390/app15052641.

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Comics are widely loved by fans around the world as a form of visual art and cultural communication. With the development of digitalization, automated comic content detection and segmentation and comic coloring systems have become important research directions for digital archiving, automatic translation, and visual content analysis. This paper proposes a composite network composed of efficient content extraction and colorization, which includes a comic extraction module and a comic colorization module based on an improved Generative Adversarial Network. It solves the problem of single perform
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Xu, Min, and YouDong Ding. "Fully automatic image colorization based on semantic segmentation technology." PLOS ONE 16, no. 11 (2021): e0259953. http://dx.doi.org/10.1371/journal.pone.0259953.

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Aiming at these problems of image colorization algorithms based on deep learning, such as color bleeding and insufficient color, this paper converts the study of image colorization to the optimization of image semantic segmentation, and proposes a fully automatic image colorization model based on semantic segmentation technology. Firstly, we use the encoder as the local feature extraction network and use VGG-16 as the global feature extraction network. These two parts do not interfere with each other, but they share the low-level feature. Then, the first fusion module is constructed to merge l
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Liu, Shiguang, and Xiang Zhang. "Automatic grayscale image colorization using histogram regression." Pattern Recognition Letters 33, no. 13 (2012): 1673–81. http://dx.doi.org/10.1016/j.patrec.2012.06.001.

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Huang, Zhitong, Nanxuan Zhao, and Jing Liao. "UniColor." ACM Transactions on Graphics 41, no. 6 (2022): 1–16. http://dx.doi.org/10.1145/3550454.3555471.

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We propose the first unified framework UniColor to support colorization in multiple modalities, including both unconditional and conditional ones, such as stroke, exemplar, text, and even a mix of them. Rather than learning a separate model for each type of condition, we introduce a two-stage colorization framework for incorporating various conditions into a single model. In the first stage, multi-modal conditions are converted into a common representation of hint points. Particularly, we propose a novel CLIP-based method to convert the text to hint points. In the second stage, we propose a Tr
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Dissertations / Theses on the topic "Automatic Colorization"

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Hati, Yliess. "Expression Créative Assistée par IA : Le Cas de La Colorisation Automatique de Line Art." Electronic Thesis or Diss., Reims, 2023. http://www.theses.fr/2023REIMS060.

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La colorisation automatique de dessins encrés est une tâche complexe pour la vision par ordinateur. Contrairement aux images en niveaux de gris, les encrages manquent d’informations sémantiques telles que les ombrages et les textures, rendant la tâche encore plus difficile.Cette thèse repose sur des travaux connexes et explore l’utilisation de architec- tures génératives modernes telles que les GAN (Réseaux Génératifs Antagonistes) et les MDD (Modèles de Diffusion par Débruitage) pour à la fois améliorer la qualité des techniques précédentes et mieux capturer l’intention de colorisa- tion de l
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Chang, Yu-wei, and 張佑瑋. "Automatic grayscale image colorization." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/89981338295360370277.

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Chen, Yung-An, and 陳勇安. "Automatic Colorization Defects Inspection using Deep Learning Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3zbtg6.

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Book chapters on the topic "Automatic Colorization"

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Tran, Tan-Bao, and Thai-Son Tran. "Automatic Natural Image Colorization." In Intelligent Information and Database Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41964-6_53.

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Larsson, Gustav, Michael Maire, and Gregory Shakhnarovich. "Learning Representations for Automatic Colorization." In Computer Vision – ECCV 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46493-0_35.

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Dhir, Rashi, Meghna Ashok, Shilpa Gite, and Ketan Kotecha. "Automatic Image Colorization Using GANs." In Soft Computing and its Engineering Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0708-0_2.

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Charpiat, Guillaume, Matthias Hofmann, and Bernhard Schölkopf. "Automatic Image Colorization Via Multimodal Predictions." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88690-7_10.

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Ding, Xiaowei, Yi Xu, Lei Deng, and Xiaokang Yang. "Colorization Using Quaternion Algebra with Automatic Scribble Generation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27355-1_12.

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Golyadkin, Maksim, and Ilya Makarov. "Semi-automatic Manga Colorization Using Conditional Adversarial Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72610-2_17.

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Kouzouglidis, Panagiotis, Giorgos Sfikas, and Christophoros Nikou. "Automatic Video Colorization Using 3D Conditional Generative Adversarial Networks." In Advances in Visual Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33720-9_16.

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Lee, Hyejin, Daehee Kim, Daeun Lee, Jinkyu Kim, and Jaekoo Lee. "Bridging the Domain Gap Towards Generalization in Automatic Colorization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19790-1_32.

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Mouzon, Thomas, Fabien Pierre, and Marie-Odile Berger. "Joint CNN and Variational Model for Fully-Automatic Image Colorization." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22368-7_42.

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Zhao, Pengcheng, Yanxiang Chen, Yang Zhao, and Zhao Zhang. "Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6594-5_5.

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Conference papers on the topic "Automatic Colorization"

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Cong, Xiaoyan, Yue Wu, Qifeng Chen, and Chenyang Lei. "Automatic Controllable Colorization via Imagination." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00252.

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Wang, Han, Xinning Chai, Yiwen Wang, Yuhong Zhang, Rong Xie, and Li Song. "Multimodal Semantic-Aware Automatic Colorization with Diffusion Prior." In 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2024. http://dx.doi.org/10.1109/icmew63481.2024.10645422.

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Makita, Koya, Akira Taguchi, and Yue Bao. "Colorization of Automatic Hologram Generation from Photographs using Machine learning." In 2024 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2024. https://doi.org/10.1109/icamechs63130.2024.10818729.

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Malah, Mehdi, Ramzi Agaba, and Faycal Abbas. "Data-driven Automatic Facial Image Colorization using an Encoder-Decoder Network." In 2025 International Symposium on iNnovative Informatics of Biskra (ISNIB). IEEE, 2025. https://doi.org/10.1109/isnib64820.2025.10983752.

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Akarawita, Isurie, and A. M. Harsha S. Abeykoon. "Automatic Colorization of Greyscale Images Using Deep Learning Fusion Technique and Accuracy Level Analysis." In 2024 International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE, 2024. http://dx.doi.org/10.1109/icarm62033.2024.10715939.

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Vidyalakshmi, R., N. Kathirvel, N. Gayathri, K. Bhuvaneshvari, M. S. Aarthi, and M. SivaramKrishnan. "Design and Evaluation of Generative Adversarial Network for Automated Image Colorization System." In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2024. http://dx.doi.org/10.1109/icosec61587.2024.10722301.

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Zhang, Guichen. "Integrating DenseNet and RDN for Enhanced Image Colorization: Development of a Novel CNN Architecture." In 2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA). IEEE, 2024. http://dx.doi.org/10.1109/aiea62095.2024.10692878.

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Watanabe, Taiki, Seitaro Shinagawa, Takuya Funatomi, et al. "Improved Automatic Colorization by Optimal Pre-colorization." In SIGGRAPH '23: Special Interest Group on Computer Graphics and Interactive Techniques Conference. ACM, 2023. http://dx.doi.org/10.1145/3588028.3603669.

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Śluzek, Andrzej. "On Unguided Automatic Colorization of Monochrome Images." In WSCG 2023 – 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. University of West Bohemia, Czech Republic, 2023. http://dx.doi.org/10.24132/csrn.3301.38.

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Image colorization is a challenging problem due to the infinite RGB solutions for a grayscale picture. Therefore, human assistance, either directly or indirectly, is essential for achieving visually plausible colorization. This paper aims to perform colorization using only a grayscale image as the data source, without any reliance on metadata or human hints. The method assumes an (arbitrary) rgb2gray model and utilizes a few simple heuristics. Despite probabilistic elements, the results are visually acceptable and repeatable, making this approach feasible (e.g. for aesthetic purposes) in domai
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Thasarathan, Harrish, Kamyar Nazeri, and Mehran Ebrahimi. "Automatic Temporally Coherent Video Colorization." In 2019 16th Conference on Computer and Robot Vision (CRV). IEEE, 2019. http://dx.doi.org/10.1109/crv.2019.00033.

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