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Journal of Artificial Intelligence and Data Mining، جلد ۹، شماره ۳، صفحات ۳۲۱-۳۲۸
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عنوان انگلیسی Automatic Grayscale Image Colorization using a Deep Hybrid Model
چکیده انگلیسی مقاله Image colorization is an interesting yet challenging task due to the descriptive nature of getting a natural-looking color image from any grayscale image. To tackle this challenge and also have a fully automatic procedure, we propose a Convolutional Neural Network (CNN)-based model to benefit from the impressive ability of CNN in the image processing tasks. To this end, we propose a deep-based model for automatic grayscale image colorization. Harnessing from convolutional-based pre-trained models, we fuse three pre-trained models, VGG16, ResNet50, and Inception-v2, to improve the model performance. The average of three model outputs is used to obtain more rich features in the model. The fused features are fed to an encoder-decoder network to obtain a color image from a grayscale input image. We perform a step-by-step analysis of different pre-trained models and fusion methodologies to include a more accurate combination of these models in the proposed model. Results on LFW and ImageNet datasets confirm the effectiveness of our model compared to state-of-the-art alternatives in the field.
کلیدواژه‌های انگلیسی مقاله Grayscale image colorization, deep learning, Convolutional neural network, Inception-v2, Color space
نویسندگان مقاله K. Kiani |
Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.

R. Hematpour |
Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.

R. Rastgoo |
Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.

نشانی اینترنتی http://jad.shahroodut.ac.ir/article_2099_56d99dec486ae27ca1e71ba2853ea374.pdf
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