Context-aware colorization of gray-scale images utilizing a cycle-consistent generative adversarial network architecture

Johari, Mohammad Mahdi; Behroozi, Hamid

doi:10.1016/j.neucom.2020.04.042

Cited by 15 publications

(5 citation statements)

References 6 publications

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“…The transdisciplinary advances within the AI domain have opened the door to automate bulks of information to be analyzed along with its context. Automation of context analysis already demonstrated success in chatbots using NLP [49,50], which may be further extended to context-aware image processing [51]. It is anticipated that progress made across the diverse fields will continue to improve with the fast-paced advances in machine learning, the scale of data and processing units along with other hardware components.…”

Section: The Smart City and Crimementioning

confidence: 99%

Enhancing City Sustainability through Smart Technologies: A Framework for Automatic Pre-Emptive Action to Promote Safety and Security Using Lighting and ICT-Based Surveillance

2020

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The scope of the present paper is to promote social, cultural and environmental sustainability in cities by establishing a conceptual framework and the relationship amongst safety in urban public space (UPS), lighting and Information and Communication Technology (ICT)-based surveillance. This framework uses available technologies and tools, as these can be found in urban equipment such as lighting posts, to enhance security and safety in UPS, ensuring protection against attempted criminal activity. Through detailed literary research, publications on security and safety concerning crime and lighting can be divided into two periods, the first one pre-1994, and the second one from 2004–2008. Since then, a significant reduction in the number of publications dealing with lighting and crime is observed, while at the same time, the urban nightscape has been reshaped with the immersion of light-emitting diode (LED) technologies. Especially in the last decade, where most municipalities in the EU28 (European Union of all the member states from the accession of Croatia in 2013 to the withdrawal of the United Kingdom in 2020) are refurbishing their road lighting with LED technology and the consideration of smart networks and surveillance is under development, the use of lighting to deter possible attempted felonies in UPS is not addressed. To capitalize on the potential of lighting as a deterrent, this paper proposes a framework that uses existing technology, namely, dimmable LED light sources, presence sensors, security cameras, as well as emerging techniques such as artificial intelligence (AI)-enabled image recognition algorithms and big data analytics and presents a possible system that could be developed as a stand-alone product to alert possible dangerous situations, deter criminal activity and promote the perception of safety thus linking lighting and ICT-based surveillance towards safety and security in UPS.

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Section: The Smart City and Crimementioning

confidence: 99%

Enhancing City Sustainability through Smart Technologies: A Framework for Automatic Pre-Emptive Action to Promote Safety and Security Using Lighting and ICT-Based Surveillance

2020

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“…Therefore, the method enhanced the colorization process. Joharia, et al [10] in this work, states that the colorization process consists of two stages; the first stage depends on an auto-encoder colorization network to determine the similarity of the images and produce an initial colored image. The second stage consists of several specialist networks, whose tasks are improving the quality of the initial colored image and producing real-looking, plausible images.…”

Section: Related Workmentioning

confidence: 99%

“…By making a comparison with previous states of art colorization models: the models of: Zhang et al [6], Jeff Hwang et al [8], and Domonkos Varga, et al [9] in all these models Convolutional Neural Networks (CNN) would be used, CNN which considered one of Artificial Neural Network ANN. While Joharia, et al [10] used the ANN by two stages in their work, and this increases the time complexity and made the performance more expensive. ANN suffers from many problems and is regarded as a less performance technique compared with SVM Nitze et al [18], SVM distinguished by multi options that are not found in ANN [5] [18].…”

Section: Performance Measurementmentioning

confidence: 99%

Support Vector Machine (SVM) for Colorization the Grayscale Image

Alrubaie

Hassoon

2020

QJES

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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 Machine to recognize the features of grayscale images which are extracted from two stages: the first stage is Haar Discrete Wavelets Transform used to configure the vector that combines with six of Statistical Measurements: (Mean, Variance, Skewness, Kurtosis, Energy and Standard Deviation) extracts from the grayscales image in the second stage. After the Support Vector Machine recognition has been done, the colorization process uses the result of Support Vector Machine to recover the color to greyscale images by using YCbCr color system then it converts the color to default color system (RGB) to be more clear. The proposed model will be able to move away from relying on the user to identify the source image which matches in color levels and it exceeds the network determinants of image types with similar color levels. In addition, Support Vector Machine is considered to be more reliable than neural networks in classification algorithms. The model performance is evaluated by using the Root Mean Squared Error (RMSE) in measuring the success of the assumed modal of matching the coloring (resulting) images and the original color images. So, a reality-related result has been obtained at a good rate for all the tested images. This model has proved to be successful in the process of recognizing the chromatic values of greyscale images then retrieving it. It takes less time complexity in trained data, and it isn’t complex in working.

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“…In the past few years, researchers have proposed various deep-learning methods for colorizing thermal infrared grayscale images, which can be roughly divided into two categories: supervised and unsupervised methods. Supervised methods [1][2][3] continued the idea of the visible light grayscale image coloring method [4][5][6][7], and reduce the gap in the pixel values of the generated image and the target RGB image by adding a loss function to better optimize the model. However, for the above methods, since the thermal infrared grayscale image coloring problem is different from the visible light grayscale image coloring problem, and the data is not strictly matched [2], so the model trained by the above methods will try to correct the positional deviation in the data, causing the model to optimize in the wrong direction.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised colorization method of thermal infrared image based on edge consistency

cai,

Tang,

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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Due to the lack of pixel level structural matching data, thermal infrared grayscale images are more difficult to color than visible and near-infrared grayscale images. Therefore, this paper proposes a unsupervised learning method based on CycleGAN. On the basis of CycleGAN, a pre trained edge monitor is introduced to calculate the edge feature map before and after image transformation, and the edge similarity loss function is calculated as the basis for optimizing the neural network parameters. The experimental results show that the proposed method effectively reduces the loss of effective edge information during the coloring process and suppresses the generation of abnormal edge information during the coloring process.

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Context-aware colorization of gray-scale images utilizing a cycle-consistent generative adversarial network architecture

Cited by 15 publications

References 6 publications

Enhancing City Sustainability through Smart Technologies: A Framework for Automatic Pre-Emptive Action to Promote Safety and Security Using Lighting and ICT-Based Surveillance

Enhancing City Sustainability through Smart Technologies: A Framework for Automatic Pre-Emptive Action to Promote Safety and Security Using Lighting and ICT-Based Surveillance

Support Vector Machine (SVM) for Colorization the Grayscale Image

An unsupervised colorization method of thermal infrared image based on edge consistency

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