Automatic Grayscale Image Colorization using a Deep Hybrid Model | ||
| Journal of AI and Data Mining | ||
| دوره 9، شماره 3، مهر 2021، صفحه 321-328 اصل مقاله (1.47 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2021.9957.2131 | ||
| نویسندگان | ||
| K. Kiani* ؛ R. Hematpour؛ R. Rastgoo | ||
| Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Grayscale image colorization؛ Deep learning؛ Convolutional Neural Network؛ Inception-v2؛ Color space | ||
| مراجع | ||
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