Fully automatic image colorization based on Convolutional Neural Network

Varga, Domonkos; Szirányi, Tamás

doi:10.1109/icpr.2016.7900208

Cited by 31 publications

(27 citation statements)

References 14 publications

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“…Thus, this shows the efficiency of the method and its future development for better work. Domonkos Varga, et al [9] presents an automatic approach that can produce reality color images from an input grayscale image. The method employs deep learning techniques depend on VGG-16 and a two-stage of Convolutional Neural Network (CNN) that predicts the channels of color.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Support Vector Machine (SVM) for Colorization the Grayscale Image

Alrubaie

Hassoon

2020

QJES

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“…Automatic colorization of grey scale images. Deep learning algorithms are trained on millions of images, and these algorithms try to learn basic colour patterns of everyday life like the “sky is blue” and “apple is red.” This information is then used to colour the detected objects in an image (Varga & Sziranyi, ; Zhang, Wang, Tao, Gong, & Zheng, ).…”

Section: Applications Of Deep Learningmentioning

confidence: 99%

A systematic review on deep learning architectures and applications

Khamparia

Singh

2019

Expert Systems

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The amount of digital data in the universe is growing at an exponential rate, doubling every 2 years, and changing how we live in the world. The information storage capacity and data requirement crossed the zettabytes. With this level of bombardment of data on machine learning techniques, it becomes very difficult to carry out parallel computations. Deep learning is broadening its scope and gaining more popularity in natural language processing, feature extraction and visualization, and almost in every machine learning trend. The purpose of this study is to provide a brief review of deep learning architectures and their working. Research papers and proceedings of conferences from various authentic resources (Institute of Electrical and Electronics Engineers, Wiley, Nature, and Elsevier) are studied and analyzed. Different architectures and their effectiveness to solve domain specific problems are evaluated. Various limitations and open problems of current architectures are discussed to provide better insights to help researchers and student to resume their research on these issues. One hundred one articles were reviewed for this meta‐analysis of deep learning. From this analysis, it is concluded that advanced deep learning architectures are combinations of few conventional architectures. For example, deep belief network and convolutional neural network are used to build convolutional deep belief network, which has higher capabilities than the parent architectures. These combined architectures are more robust to explore the problem space and thus can be the answer to build a general‐purpose architecture.

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“…Iizuka et al [19] elaborated a colorization method that jointly extracts global and local features from an image and then merge them together. In [20], the authors proposed a fully automatic algorithm based on VGG-16 [21] and a two-stage Convolutional Neural Network to provide richer representation by adding semantic information from a preceding layer. Furthermore, the authors proposed Quaternion Structural Similarity [22] for quality evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…As pointed out in many papers [20], [23], [24], [26], Euclidean loss function is not an optimal solution because it will result in the so-called averaging problem. Namely, the system will produce grayish sepia tone effects.…”

Section: Our Approachmentioning

confidence: 99%

Twin Deep Convolutional Neural Network for Example-Based Image Colorization

Varga

Szirányi

2017

Computer Analysis of Images and Patterns

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Abstract. This paper deals with the colorization of grayscale images. Recent papers have shown remarkable results on image colorization utilizing various deep architectures. Unlike previous methods, we perform colorization using a deep architecture and a reference image. Our architecture utilizes two parallel Convolutional Neural Networks which have the same structure. One CNN, which uses the reference image, helps the other CNN in color prediction for the input image. On the other hand, the second CNN, which uses the input image, helps to identify the areas which holds essential information about the color scheme of the scene. Comprehensive experiments and qualitative and quantitative evaluations were conducted on the images of SUN database and on other images. Quantitative evaluations are based on Peak Signal-to-Noise Ratio (PSNR) and on Quaternion Structural Similarity (QSSIM).

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Fully automatic image colorization based on Convolutional Neural Network

Cited by 31 publications

References 14 publications

Support Vector Machine (SVM) for Colorization the Grayscale Image

Support Vector Machine (SVM) for Colorization the Grayscale Image

A systematic review on deep learning architectures and applications

Twin Deep Convolutional Neural Network for Example-Based Image Colorization

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