Robust single-image super-resolution using cross-scale self-similarity

We present a noise-aware single-image super-resolution (SI-SR) algorithm, which automatically cancels additive noise while adding detail learned from lower-resolution scales. In contrast with most SI-SR techniques, we do not assume the input image to be a clean source of examples. Instead, we adapt the recent and efficient in-place cross-scale self-similarity prior for both learning fine detail examples and reducing image noise. Our experiments show a promising performance, despite the relatively simple algorithm. Both objective evaluations and subjective validations show clear quality improvements when upscaling noisy images.