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Found 2 papers, 1 papers with code
Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring
In this paper, we develop a data-driven approach to model the saturated pixels by a learned latent map.
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i. e. first generating synthetic low-resolution (LR) images $\mathcal{Y}^g$ corresponding to real-world high-resolution (HR) images $\mathcal{X}^r$ in the real-world LR domain $\mathcal{Y}^r$, and then utilizing the pseudo pairs $\{\mathcal{Y}^g, \mathcal{X}^r\}$ for training in a supervised manner.
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