Deep neural network joint multi-scale attention for remote sensing image colorization

Image colorization is a technology to transform gray-scale image into natural color image, which is always a challenging task in image processing. In this paper, we design a deep neural network model including multi-scale convolutional down-sampling and attention mechanism, which can effectively solve the problem of coloring gray remote sensing image. First, we input the grayscale remote sensing image into the feature extractor of our model for feature extraction and multi-scale down-sampling. Second, the feature map extracted by the feature extractor is input into the squeeze-and-excitation attention mechanism module to enhance the extraction of key information. Finally, the feature map output by the feature extractor and squeeze-and-excitation module is input into the feature reconstructor for feature reconstruction and color restoration, and then color remote sensing images are obtained. Unlike the conventional methods, our method is an end-to-end model, and without any human interaction in the coloring process. The experimental results show that the color remote sensing image generated by our method is superior to the existing methods in both subjective and objective metrics.