Deep Image Super Resolution via Natural Image Priors
This addresses a specific issue in computer vision for applications requiring high-quality image upscaling with limited data, representing an incremental improvement.
The paper tackles the problem of single image super-resolution performance degradation when training data is limited by regularizing deep networks with natural image priors, resulting in improved effectiveness in training-starved regimes.
Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature. Convolutional neural networks (CNNs) are typically learned to represent the mapping between low-resolution (LR) and high-resolution (HR) images/patches with the help of training examples. Most existing deep networks for SR produce high quality results when training data is abundant. However, their performance degrades sharply when training is limited. We propose to regularize deep structures with prior knowledge about the images so that they can capture more structural information from the same limited data. In particular, we incorporate in a tractable fashion within the CNN framework, natural image priors which have shown to have much recent success in imaging and vision inverse problems. Experimental results show that the proposed deep network with natural image priors is particularly effective in training starved regimes.