Learning a Single Convolutional Super-Resolution Network for
Multiple Degradations
Abstract
Recent years have witnessed the unprecedented success
of deep convolutional neural networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based
SISR methods mostly assume that a low-resolution (LR) image is bicubicly downsampled from a high-resolution (HR)
image, thus inevitably giving rise to poor performance when
the true degradation does not follow this assumption. Moreover, they lack scalability in learning a single model to nonblindly deal with multiple degradations. To address these
issues, we propose a general framework with dimensionality stretching strategy that enables a single convolutional
super-resolution network to take two key factors of the SISR
degradation process, i.e., blur kernel and noise level, as input. Consequently, the super-resolver can handle multiple
and even spatially variant degradations, which significantly
improves the practicability. Extensive experimental results on synthetic and real LR images show that the proposed
convolutional super-resolution network not only can produce favorable results on multiple degradations but also is
computationally efficient, providing a highly effective and
scalable solution to practical SISR applications