Automatic Grayscale Image Colorization using a Deep Hybrid Model
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JADM-9-3_005
تاریخ نمایه سازی: 18 مهر 1400
Abstract:
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, VGG۱۶, ResNet۵۰, and Inception-v۲, 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.
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Authors
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.
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