Deep Image Super Resolution via Natural Image Priors

TL;DR

This paper introduces a deep learning approach for single image super-resolution that leverages natural image priors to improve performance especially when training data is limited.

Contribution

It proposes integrating natural image priors into CNNs to enhance super-resolution results under limited training data conditions.

Findings

Improved super-resolution quality with limited training data.

Natural image priors help CNNs capture more structural information.

Method outperforms traditional CNNs without priors in low-data regimes.

Abstract

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.