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Journal articles on the topic 'Automatic Colorization'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

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  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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K. El .Abbadi, Nidhal, and Eman Saleem. "Automatic image colorization based on SVD and lab color space." International Journal of Engineering & Technology 8, no. 2 (2019): 63–71. http://dx.doi.org/10.14419/ijet.v7i4.26186.

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Images colorization is the operation of append colors to the grayscale image by using a reference color image. The main problem ofÂ coloring a grayscale image involves constructing three dimensional image from one dimensional array. The current paper developed a fully automatic image colorization system based on the singular value decomposition (SVD) transformation. SVD used to measuring the best pixel in the reference image proper to colorize the gray image, depending on comparing the pixel value and their neighbor values of the gray image with pixel value and their neighbors in the reference

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Furusawa, Chie. "2-1 Colorization Techniques for Manga and Line Drawings; Comicolorization: Semi-Automatic Manga Colorization." Journal of The Institute of Image Information and Television Engineers 72, no. 5 (2018): 347–52. http://dx.doi.org/10.3169/itej.72.347.

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Sugumar, S. J. "Colorization of Digital Images: An Automatic and Efficient Approach through Deep learning." Journal of Innovative Image Processing 4, no. 3 (2022): 183–94. http://dx.doi.org/10.36548/jiip.2022.3.006.

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Colorization is not a guaranteed, but a feasible mapping between intensity and chrominance values. This paper presents a colorization system that draws inspiration from recent developments in deep learning and makes use of both locally and globally relevant data. One such property is the rarity of each color category on the quantized plane. The denoising model contains hybrid approach with cluster normalization through U-Net deep learning construction of framework. These are built on the basic U-Net design for segmentation. To eliminate gaussian noise in digital images, this article has develo

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Serebryanaya, L. V., and V. V. Potaraev. "Automatic Image Colorization Based on Convolutional Neural Networks." Digital Transformation, no. 2 (July 11, 2020): 58–64. http://dx.doi.org/10.38086/2522-9613-2020-2-58-64.

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Zhang, Zuyu, Yan Li, and Byeong-Seok Shin. "Robust Medical Image Colorization with Spatial Mask-Guided Generative Adversarial Network." Bioengineering 9, no. 12 (2022): 721. http://dx.doi.org/10.3390/bioengineering9120721.

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Color medical images provide better visualization and diagnostic information for doctors during clinical procedures than grayscale medical images. Although generative adversarial network-based image colorization approaches have shown promising results, in these methods, adversarial training is applied to the whole image without considering the appearance conflicts between the foreground objects and the background contents, resulting in generating various artifacts. To remedy this issue, we propose a fully automatic spatial mask-guided colorization with generative adversarial network (SMCGAN) f

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Lee, Yeongseop, and Seongjin Lee. "Automatic Colorization of Anime Style Illustrations Using a Two-Stage Generator." Applied Sciences 10, no. 23 (2020): 8699. http://dx.doi.org/10.3390/app10238699.

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Line-arts are used in many ways in the media industry. However, line-art colorization is tedious, labor-intensive, and time consuming. For such reasons, a Generative Adversarial Network (GAN)-based image-to-image colorization method has received much attention because of its promising results. In this paper, we propose to use color a point hinting method with two GAN-based generators used for enhancing the image quality. To improve the coloring performance of drawing with various line styles, generator takes account of the loss of the line-art. We propose a Line Detection Model (LDM) which is

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Wang, Zhiyuan, Yi Yu, Daqun Li, Yuanyuan Wan, and Mingyang Li. "Colorful Image Colorization with Classification and Asymmetric Feature Fusion." Sensors 22, no. 20 (2022): 8010. http://dx.doi.org/10.3390/s22208010.

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An automatic colorization algorithm can convert a grayscale image to a colorful image using regression loss functions or classification loss functions. However, the regression loss function leads to brown results, while the classification loss function leads to the problem of color overflow and the computation of the color categories and balance weights of the ground truth required for the weighted classification loss is too large. In this paper, we propose a new method to compute color categories and balance the weights of color images. In this paper, we propose a new method to compute color

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Attea, Bara'a Ali, and Sana'a Khudayer Jaddwa Al-Janaby. "A FULLY AUTOMATIC GENETIC APPROACH FOR GRAYSCALE IMAGE COLORIZATION." Journal of Engineering 12, no. 02 (2006): 237–45. http://dx.doi.org/10.31026/j.eng.2006.02.05.

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Colorization is a computer assisted process of adding color to a monochrome (grayscale) image ormovie. The early published methods to perform the image colorizing rely on heuristic techniquesfor choosing RGB colors from a global palette and applying them to regions of the target grayscaledimage. The main improvement of the proposed technique is the adoption in a fully automaticway the genetic algorithm as an efficient search method to find best match for each pixel in thetarget image. The proposed genetic algorithm evolves a population of randomly selected individuals (that represents a possib

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Zhang, Jiangning, Chao Xu, Jian Li, et al. "SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-resolution." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3271–79. http://dx.doi.org/10.1609/aaai.v36i3.20236.

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In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-sampling operation for the target device. However, this pipeline is redundant and inefficient for the independent processes, and some inner features could have been shared. Therefore, we present an efficient paradigm to perform Simultaneously Image Colorization and Super-resolution (SCS) and propose an end-to-end SCSNet to achieve this goal. The proposed method consists of two parts: colorization branch for learning colo

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Kenji, Sakoma, and Sakamoto Makoto. "For Colorization using Template Matching Basic Research on." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 517–19. https://doi.org/10.35940/ijrte.C4628.099320.

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Colorization, also known as colorization, is a term introduced by Wilson Markle in 1970, and is a method of coloring black-and-white images and videos using a computer. Coloring is important. Imagine coloring a picture as an example. The painting before painting only gives information of existence, such as trees, flowers, and clouds. Some things can be identified by color. This does not give us enough information from the picture. But what about coloring? If you paint the sky red, it will be a sunset, and if you paint the ground green, it will be a meadow. In other words, it is possible to exp

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Lee, Yeongseop, and Seongjin Lee. "Service Platform for Serving Line-art Automatic Colorization Model." TRANSACTION OF THE KOREAN INSTITUTE OF ELECTRICAL ENGINEERS P 71, no. 1 (2022): 41–47. http://dx.doi.org/10.5370/kieep.2022.71.1.41.

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Abbadi, Nidhal K. El, and Eman Saleem Razaq. "Automatic gray images colorization based on lab color space." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (2020): 1501. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1501-1509.

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<p>The colorization aim to transform a black and white image to a color image. This is a very hard issue and usually requiring manual intervention by the user to produce high-quality images free of artifact. The public problem of inserting gradients color to a gray image has no accurate method. The proposed method is fully automatic method. We suggested to use reference color image to help transfer colors from reference image to gray image. The reference image converted to Lab color space, while the gray scale image normalized according to the lightness channel L. the gray image concaten

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Haji-Esmaeili, Mohammad Mahdi, and Gholamali Montazer. "Automatic Colorization of Grayscale Images Using Generative Adversarial Networks." Signal and Data Processing 16, no. 1 (2019): 57–74. http://dx.doi.org/10.29252/jsdp.16.1.57.

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Chen, Changjian, Yi Xu, and Xiaokang Yang. "User tailored colorization using automatic scribbles and hierarchical features." Digital Signal Processing 87 (April 2019): 155–65. http://dx.doi.org/10.1016/j.dsp.2019.01.021.

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Zhang, Wei, Chao-Wei Fang, and Guan-Bin Li. "Automatic Colorization with Improved Spatial Coherence and Boundary Localization." Journal of Computer Science and Technology 32, no. 3 (2017): 494–506. http://dx.doi.org/10.1007/s11390-017-1739-6.

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Nidhal, K. EL Abbadi, and Saleem Eman. "Automatic gray images colorization based on lab color space." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 3 (2020): 1501–9. https://doi.org/10.11591/ijeecs.v18.i3.pp1501-1509.

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The colorization aims to transform a black and white image to a color image. This is a very hard issue and usually requiring manual intervention by the user to produce high-quality images free of artifact. The public problem of inserting gradients color to a gray image has no accurate method. The proposed method is fully automatic method. We suggested to use reference color image to help transfer colors from reference image to gray image. The reference image converted to Lab color space, while the gray scale image normalized according to the lightness channel L. The gray image concatenates wit

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Schmitt, M., L. H. Hughes, M. Körner, and X. X. Zhu. "COLORIZING SENTINEL-1 SAR IMAGES USING A VARIATIONAL AUTOENCODER CONDITIONED ON SENTINEL-2 IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 1045–51. http://dx.doi.org/10.5194/isprs-archives-xlii-2-1045-2018.

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In this paper, we have shown an approach for the automatic colorization of SAR backscatter images, which are usually provided in the form of single-channel gray-scale imagery. Using a deep generative model proposed for the purpose of photograph colorization and a Lab-space-based SAR-optical image fusion formulation, we are able to predict artificial color SAR images, which disclose much more information to the human interpreter than the original SAR data. Future work will aim at further adaption of the employed procedure to our special case of multi-sensor remote sensing imagery. Furthermore,

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Petre, Cosmin-Gheorghe, and Stefan Trausan-Matu. "Automatic black and white image colorization using generative adversarial networks." International Joural of User-System Interaction 13, no. 2 (2020): 110–20. http://dx.doi.org/10.37789/ijusi.2020.13.2.4.

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Othman, Omar Abdulwahhab, Sait Ali Uymaz, and Betül Uzbaş. "Automatic Black & White Images colorization using Convolutional neural network." Academic Perspective Procedia 2, no. 3 (2019): 1189–95. http://dx.doi.org/10.33793/acperpro.02.03.131.

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In this paper, automatic black and white image colorization method has been proposed. The study is based on the best-known deep learning algorithm CNN (Convolutional neural network). The Model that developed taking the input in gray scale and predict the color of image based on the dataset that trained on it. The color space used in this work is Lab Color space the model takes the L channel as the input and the ab channels as the output. The Image Net dataset used and random selected image have been used to construct a mini dataset of images that contains 39,604 images splitted into 80% traini

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Poterek, Quentin, Pierre-Alexis Herrault, Grzegorz Skupinski, and David Sheeren. "Deep Learning for Automatic Colorization of Legacy Grayscale Aerial Photographs." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 2899–915. http://dx.doi.org/10.1109/jstars.2020.2992082.

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Salmona, Antoine, Lucía Bouza, and Julie Delon. "DeOldify: A Review and Implementation of an Automatic Colorization Method." Image Processing On Line 12 (September 5, 2022): 347–68. http://dx.doi.org/10.5201/ipol.2022.403.

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Li, Bo, Yu-Kun Lai, and Paul L. Rosin. "Example-based image colorization via automatic feature selection and fusion." Neurocomputing 266 (November 2017): 687–98. http://dx.doi.org/10.1016/j.neucom.2017.05.083.

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S. Hovhannisyan, S. Hovhannisyan, and S. Agaian S. Agaian. "Thermal Image Enhancement and Semantic Multi-Task Learning for Accurate Infrared Colorization." International Journal of Advances in Engineering and Management 7, no. 6 (2025): 237–45. https://doi.org/10.35629/5252-0706237245.

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Thermal infrared imaging plays a vital role in computer vision applications, especially under difficult conditions such as low light and poor visibility, where traditional RGB cameras struggle. Transforming night-vision thermal images to resemble daytime colors is essential for better scene interpretation and decision-making in surveillance, autonomous navigation, and search-and-rescue operations. Nonetheless, overcoming the significant modality gap between visual and thermal cameras is still a challenge, as they capture fundamentally different physical properties of the same scene, thermal ra

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Singh, Archana, Ashutosh Choubey, Anant Kumar, and Ashvani Singh. "Automatic Colorizing Black and White Image System Using Deep Learning." International Journal of Innovative Research in Advanced Engineering 11, no. 11 (2024): 876–83. https://doi.org/10.26562/ijirae.2024.v1111.13.

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Colorization involves transforming a grayscale (black-and-white) image into a colorized version that reflects the semantic color tones of the original input. Over recent years, this process has garnered substantial attention, with significant advancements achieved by researchers in the field. The technique has numerous applications, such as in medical imaging and the restoration of historical artifacts. Various methods have been developed to address this challenge, leveraging technologies like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). These models differ

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Bartenyev, Oleg V., and Emil R. Salakhutdinov. "Sketch Colorization Based on Generative-Adversarial Neural Networks." Vestnik MEI, no. 1 (2022): 120–29. http://dx.doi.org/10.24160/1993-6982-2022-1-120-129.

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Generative adversarial neural networks (GAN) successfully perform automatic colorization of character sketches. Such models can be further improved in such aspects as increasing the throughput, improving the colorization quality, and reducing the model size. Steps aimed at improving the colorization quality are taken. Known solutions are reviewed, and an initial GAN model with eight blocks in the generator encoder and decoder is developed and trained based on the review results. The second GAN model is obtained on the basis of the first one by including residual blocks in the generator encoder

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Li, Bo, Yu-Kun Lai, Matthew John, and Paul L. Rosin. "Automatic Example-Based Image Colorization Using Location-Aware Cross-Scale Matching." IEEE Transactions on Image Processing 28, no. 9 (2019): 4606–19. http://dx.doi.org/10.1109/tip.2019.2912291.

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Oishi, Shuji, and Ryo Kurazume. "Manual/automatic colorization for three-dimensional geometric models utilizing laser reflectivity." Advanced Robotics 28, no. 24 (2014): 1637–51. http://dx.doi.org/10.1080/01691864.2014.968616.

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Chybicki, Mariusz, Wiktor Kozakiewicz, Dawid Sielski, and Anna Fabijańska. "Deep cartoon colorizer: An automatic approach for colorization of vintage cartoons." Engineering Applications of Artificial Intelligence 81 (May 2019): 37–46. http://dx.doi.org/10.1016/j.engappai.2019.02.006.

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Ambadkar, Tanmay, and Jignesh S. Bhatt. "A Simple Fast Resource-efficient Deep Learning for Automatic Image Colorization." Color and Imaging Conference 31, no. 1 (2023): 126–31. http://dx.doi.org/10.2352/cic.2023.31.1.24.

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Luo, Hui, and Qiang Zeng. "Study on the Application of Visual Communication Design in APP Interface Design in the Context of Deep Learning." Computational Intelligence and Neuroscience 2022 (June 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/9262676.

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Visual communication concepts enable linguistics or semiotics to the teaching of visual communication designs, creating graphic designs into an innovative and scientific discipline. The use of storyline techniques in visual communication not only inspires the imagination of designer but also arouses the visual memory of the audience. Besides, improving cultural heritage such as historical images is important to protect cultural diversity. Recently, the developments of deep learning (DL) and computer vision (CV) approaches make it possible for the automatic colorization of grayscale images into

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Li, Hui, Wei Zeng, Guorong Xiao, and Huabin Wang. "The Instance-Aware Automatic Image Colorization Based on Deep Convolutional Neural Network." Intelligent Automation & Soft Computing 26, no. 4 (2020): 841–46. http://dx.doi.org/10.32604/iasc.2020.010118.

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Chen, Shu-Chuan, and Aaron Ogata. "MixtureTree Annotator: A Program for Automatic Colorization and Visual Annotation of MixtureTree." PLOS ONE 10, no. 3 (2015): e0118893. http://dx.doi.org/10.1371/journal.pone.0118893.

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Alrubaie, Shymaa Akram, and Israa Mohammed Hassoon. "Support Vector Machine (SVM) for Colorization the Grayscale Image." Al-Qadisiyah Journal for Engineering Sciences 13, no. 3 (2020): 207–14. http://dx.doi.org/10.30772/qjes.v13i3.658.

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Recently, there have been several automatic approaches to color grayscale images, which depend on the internal features of the grayscale images. There are several scales which are considered as a prominent key to extract the corresponding chromatic value of the gray level. In this aspect, colorizing methods that rely on automatic algorithms are still under investigation, especially after the development of neural networks used to recognize the features of images. This paper develops a new model to obtain a color image from an original grayscale image through the use of the Support Vector Machi

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Li, Ao, and Zhongsheng Wang. "Research on Image Colorization Based on Deep Learning." Journal of Physics: Conference Series 2872, no. 1 (2024): 012020. http://dx.doi.org/10.1088/1742-6596/2872/1/012020.

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Abstract Gray scale picture colorization, a research hotspot in the domain of computer vision, seeks to assign reasonable colours to every single pixel of a grayscale picture, enriching the visual information of the coloured picture. Neural networks take grayscale images as input and output colorized images. From a psychological perspective, colours can provide observers with a more pleasant perceptual experience. Old photos can also be coloured, which are important documentary resources for recording real history and restoring the social landscape of the time. Additionally, due to the limitat

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Koleini, Mina, S. Amirhassan Monadjemi, and Payman Moallem. "Automatic black and white film colorization using texture features and artificial neural networks." Journal of the Chinese Institute of Engineers 33, no. 7 (2010): 1049–57. http://dx.doi.org/10.1080/02533839.2010.9671693.

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Seo, Chang Wook, and Yongduek Seo. "Seg2pix: Few Shot Training Line Art Colorization with Segmented Image Data." Applied Sciences 11, no. 4 (2021): 1464. http://dx.doi.org/10.3390/app11041464.

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There are various challenging issues in automating line art colorization. In this paper, we propose a GAN approach incorporating semantic segmentation image data. Our GAN-based method, named Seg2pix, can automatically generate high quality colorized images, aiming at computerizing one of the most tedious and repetitive jobs performed by coloring workers in the webtoon industry. The network structure of Seg2pix is mostly a modification of the architecture of Pix2pix, which is a convolution-based generative adversarial network for image-to-image translation. Through this method, we can generate

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Tian, Nannan, Yuan Liu, Bo Wu, and Xiaofeng Li. "Colorization of Logo Sketch Based on Conditional Generative Adversarial Networks." Electronics 10, no. 4 (2021): 497. http://dx.doi.org/10.3390/electronics10040497.

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Logo design is a complex process for designers and color plays a very important role in logo design. The automatic colorization of logo sketch is of great value and full of challenges. In this paper, we propose a new logo design method based on Conditional Generative Adversarial Networks, which can output multiple colorful logos only by providing one logo sketch. We improve the traditional U-Net structure, adding channel attention and spatial attention in the process of skip-connection. In addition, the generator consists of parallel attention-based U-Net blocks, which can output multiple logo

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Tan, Cong, and Shaoyu Yang. "Automatic Extraction of Color Features from Landscape Images Based on Image Processing." Traitement du Signal 38, no. 3 (2021): 747–55. http://dx.doi.org/10.18280/ts.380322.

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The dominant color features determine the presentation effect and visual experience of landscapes. The existing studies rarely quantify the application effect of landscape colors through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space. To solve the problem, this paper proposes an automatic extraction method for color features from landscape images based on image processing. Firstly, a landscape lighting model was constructed based on color constancy theories, and the quality of landscape images was improved wit

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Viana, Monique Simplicio, Orides Morandin Junior, and Rodrigo Colnago Contreras. "An Improved Local Search Genetic Algorithm with a New Mapped Adaptive Operator Applied to Pseudo-Coloring Problem." Symmetry 12, no. 10 (2020): 1684. http://dx.doi.org/10.3390/sym12101684.

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In many situations, an expert must visually analyze an image arranged in grey levels. However, the human eye has strong difficulty in detecting details in this type of image, making it necessary to use artificial coloring techniques. The pseudo-coloring problem (PsCP) consists of assigning to a grey-level image, pre-segmented in K sub-regions, a set of K colors that are as dissimilar as possible. This problem is part of the well-known class of NP-Hard problems and, therefore, does not present an exact solution for all instances. Thus, meta-heuristics has been widely used to overcome this probl

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Ibraheem, Baraa Qasim, Kassem Danach, and Ahmad Ghandour. "Colorization of Black and White Images Using a Hybrid Deep Learning Framework." International Research Journal of Innovations in Engineering and Technology 08, no. 05 (2024): 06–11. http://dx.doi.org/10.47001/irjiet/2024.805002.

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With the development of deep learning algorithms and their great success in the field of computer vision, the field of automatic image colorization has witnessed significant improvements in accuracy and realism. This study introduces a novel deep learning-based method for colorizing black and white photographs, utilizing the powerful feature extraction of the InceptionResNetV2 model and the generative capabilities of autoencoders. A custom data generator was developed for efficient preprocessing, data augmentation, and batch processing, enhancing memory usage and scalability. The system encode

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