  Despite several solutions and experiments have been conducted recently
addressing image super-resolution (SR), boosted by deep learning (DL)
techniques, they do not usually design evaluations with high scaling factors,
capping it at 2x or 4x. Moreover, the datasets are generally benchmarks which
do not truly encompass significant diversity of domains to proper evaluate the
techniques. It is also interesting to remark that blind SR is attractive for
real-world scenarios since it is based on the idea that the degradation process
is unknown, and hence techniques in this context rely basically on
low-resolution (LR) images. In this article, we present a high-scale (8x)
controlled experiment which evaluates five recent DL techniques tailored for
blind image SR: Adaptive Pseudo Augmentation (APA), Blind Image SR with
Spatially Variant Degradations (BlindSR), Deep Alternating Network (DAN),
FastGAN, and Mixture of Experts Super-Resolution (MoESR). We consider 14 small
datasets from five different broader domains which are: aerial, fauna, flora,
medical, and satellite. Another distinctive characteristic of our evaluation is
that some of the DL approaches were designed for single-image SR but others
not. Two no-reference metrics were selected, being the classical natural image
quality evaluator (NIQE) and the recent transformer-based multi-dimension
attention network for no-reference image quality assessment (MANIQA) score, to
assess the techniques. Overall, MoESR can be regarded as the best solution
although the perceptual quality of the created HR images of all the techniques
still needs to improve. Supporting code: https://github.com/vsantjr/DL_BlindSR.
Datasets:
https://www.kaggle.com/datasets/valdivinosantiago/dl-blindsr-datasets.
