[R] Hitchhiker’s Guide to Super-Resolution: Introduction and Recent Advances

Submitted by Maleficent_Stay_7737 t3_11287zf in MachineLearning

I'm glad to share with you our Open Access survey paper about image super-resolution:
https://ieeexplore.ieee.org/abstract/document/10041995

The goal of this work is to give an overview of the abundance of publications in image super-resolution, give an introduction for new researchers, and open thriving discussions as well as point to potential future directions to advance the field :)

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tdgros t1_j8j2wbd wrote

Unless I missed it, the paper does mention the fact that the degradation mapping should be estimated but does not detail or cite papers that do that. (examples: KernelGAN, KernelNet, doubleDIP or MetaKernelGAN...)

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Maleficent_Stay_7737 OP t1_j8kezw2 wrote

Thank you very much for your comment. It is a very valuable and important note for the subject and community as this is a super important aspect of image SR. We refer to this topic under the Unsupervised SR section (8) but did not have the space to go into more detail, which doesn't mean it doesn't deserve attention. We referenced another survey by Liu et al. (“Blind image superresolution: A survey and beyond", https://arxiv.org/abs/2107.03055) from 2022 to fill this gap (also mentions KernelGAN and related methods), which we find is an informative source for blind SR in general.

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super544 t1_j8kokxc wrote

Does SR include deblurring?

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Maleficent_Stay_7737 OP t1_j8ktr7x wrote

Not exactly. Both are formulated as inverse problem in image processing. Super-Resolution investigates the case where information is lost due to downscaling whereas deblurring focus on blurry input (e.g., by low pass filters). However, they have similar properties and deep learning based methods can be applied to both. In this survey, we didn't go deeper into the deblurring topic.

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