  Since the first success of Dong et al., the deep-learning-based approach has
become dominant in the field of single-image super-resolution. This replaces
all the handcrafted image processing steps of traditional sparse-coding-based
methods with a deep neural network. In contrast to sparse-coding-based methods,
which explicitly create high/low-resolution dictionaries, the dictionaries in
deep-learning-based methods are implicitly acquired as a nonlinear combination
of multiple convolutions. One disadvantage of deep-learning-based methods is
that their performance is degraded for images created differently from the
training dataset (out-of-domain images). We propose an end-to-end
super-resolution network with a deep dictionary (SRDD), where a high-resolution
dictionary is explicitly learned without sacrificing the advantages of deep
learning. Extensive experiments show that explicit learning of high-resolution
dictionary makes the network more robust for out-of-domain test images while
maintaining the performance of the in-domain test images.
