Abstract
This paper proposes a multi-scale contrastive learning technique for image colorization. The image colorization task aims to find a map between the source gray image and the predicted color image. Multi-scale contrastive learning for unpaired image colorization has been proposed to transform the gray patches in the input image into the color patches in the output image. The contrastive learning method uses input and output patches and maximizes their mutual information to infer an efficient mapping between the two domains. We propose a multi-scale approach for contrastive learning where the contrastive loss is determined from different resolutions of the source image to improve the color quality. We further illustrate the effectiveness of the proposed approach through experimental outcomes and comparisons with cutting-edge strategies in image colorization tasks.
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Lambat, K., Ghorai, M. (2023). Multi-scale Contrastive Learning for Image Colorization. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_27
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DOI: https://doi.org/10.1007/978-981-19-7867-8_27
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