High-dimensional Assisted Generative Model for Color Image Restoration

• URL: http://arxiv.org/abs/2108.06460v1
• Date: Sat, 14 Aug 2021 04:05:29 GMT
• Title: High-dimensional Assisted Generative Model for Color Image Restoration
• Authors: Kai Hong, Chunhua Wu, Cailian Yang, Minghui Zhang, Yancheng Lu, Yuhao Wang, and Qiegen Liu
• Abstract summary: This work presents an unsupervised deep learning scheme that exploits high-dimensional assisted score-based generative model for color image restoration tasks. Considering the sample number and internal dimension in score-based generative model, two different high-dimensional ways are proposed: The channel-copy transformation increases the sample number and the pixel-scale transformation decreases feasible dimension space. To alleviate the difficulty of learning high-dimensional representation, a progressive strategy is proposed to leverage the performance.
• Score: 12.459091135428885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents an unsupervised deep learning scheme that exploiting high-dimensional assisted score-based generative model for color image restoration tasks. Considering that the sample number and internal dimension in score-based generative model have key influence on estimating the gradients of data distribution, two different high-dimensional ways are proposed: The channel-copy transformation increases the sample number and the pixel-scale transformation decreases feasible space dimension. Subsequently, a set of high-dimensional tensors represented by these transformations are used to train the network through denoising score matching. Then, sampling is performed by annealing Langevin dynamics and alternative data-consistency update. Furthermore, to alleviate the difficulty of learning high-dimensional representation, a progressive strategy is proposed to leverage the performance. The proposed unsupervised learning and iterative restoration algo-rithm, which involves a pre-trained generative network to obtain prior, has transparent and clear interpretation compared to other data-driven approaches. Experimental results on demosaicking and inpainting conveyed the remarkable performance and diversity of our proposed method.

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