Automatic Grayscale Image Colorization using a Deep Hybrid Model

K. Kiani, R. Hematpour, R. Rastgoo
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引用次数: 5

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, 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.
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使用深度混合模型的自动灰度图像着色
图像着色是一项有趣但具有挑战性的任务,因为从任何灰度图像中获得自然的彩色图像具有描述性。为了解决这一挑战并实现全自动过程,我们提出了一种基于卷积神经网络(CNN)的模型,以受益于CNN在图像处理任务中的出色能力。为此,我们提出了一种基于深度的灰度图像自动着色模型。利用基于卷积的预训练模型,我们融合了三个预训练模型,VGG16, ResNet50和Inception-v2,以提高模型的性能。利用三个模型输出的平均值来获得模型中更丰富的特征。将融合特征馈送到编码器-解码器网络,以从灰度输入图像获得彩色图像。我们对不同的预训练模型和融合方法进行了一步一步的分析,以在建议的模型中包含这些模型的更准确的组合。LFW和ImageNet数据集的结果证实了我们的模型与该领域最先进的替代方案相比的有效性。
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