High-dimensional Assisted Generative Model for Color Image Restoration
arXiv:2108.06460
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.
arXiv comment12 pages,11 figures
References in corpus (10)
- Auto-Encoding Variational Bayes
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- Semi-Supervised Learning with Deep Generative Models
- Deep Convolutional Inverse Graphics Network
- Perceptual Adversarial Networks for Image-to-Image Transformation
- PEPSI++: Fast and Lightweight Network for Image Inpainting
- Generative Face Completion
- The Little Engine that Could: Regularization by Denoising (RED)
- Generative Modeling with Denoising Auto-Encoders and Langevin Sampling