Survey of deep learning for image super-resolution with high magnification and unknown distortion
Survey of deep learning for image super-resolution with high magnification and unknown distortion
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluation
arXiv paper abstract https://arxiv.org/abs/2306.09426
arXiv PDF paper https://arxiv.org/pdf/2306.09426.pdf
Despite several ... 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.
... 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).
... consider 14 small datasets from five different broader domains which are: aerial, fauna, flora, medical, and satellite.
... 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 ...
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