Wavelet Transform-assisted Adaptive Generative Modeling for Colorization
نویسندگان
چکیده
Unsupervised deep learning has recently demonstrated the promise to produce high-quality samples. While it tremendous potential promote image colorization task, performance is limited owing high-dimension of data manifold and model capability. This study presents a novel scheme that exploiting score-based generative in wavelet domain address issue. By taking advantage multi-scale multi-channel representation via transform, proposed learns priors from stacked coefficient components, thus characteristics under coarse detail frequency spectrums jointly effectively. Moreover, such highly flexible without adversarial optimization can execute tasks better dual consistency terms domain, namely data-consistency structure-consistency. Specifically, training phase, set tensors consisting coefficients are used as input train network by denoising score matching. In test samples iteratively generated annealed Langevin dynamics with structure consistencies. Experiments remarkable improvements on quality, particularly robustness diversity.
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ژورنال
عنوان ژورنال: IEEE Transactions on Multimedia
سال: 2022
ISSN: ['1520-9210', '1941-0077']
DOI: https://doi.org/10.1109/tmm.2022.3177933