Traditional blind image SR methods need to model real-world degradations
precisely. Consequently, current research struggles with this dilemma by
assuming idealized degradations, which leads to limited applicability to actual
user data. Moreover, the ideal scenario - training models on data from the
targeted user base - presents significant privacy concerns. To address both
challenges, we propose to fuse image SR with federated learning, allowing
real-world degradations to be directly learned from users without invading
their privacy. Furthermore, it enables optimization across many devices without
data centralization. As this fusion is underexplored, we introduce new
benchmarks specifically designed to evaluate new SR methods in this federated
setting. By doing so, we employ known degradation modeling techniques from SR
research. However, rather than aiming to mirror real degradations, our
benchmarks use these degradation models to simulate the variety of degradations
found across clients within a distributed user base. This distinction is
crucial as it circumvents the need to precisely model real-world degradations,
which limits contemporary blind image SR research. Our proposed benchmarks
investigate blind image SR under new aspects, namely differently distributed
degradation types among users and varying user numbers. We believe new methods
tested within these benchmarks will perform more similarly in an application,
as the simulated scenario addresses the variety while federated learning
enables the training on actual degradations.

将图像超分辨率与联邦学习相结合，从用户中直接学习真实世界中的退化，既不侵犯用户隐私，又可以在多个设备上进行优化。为了评估新的超分辨率方法，我们提出了新的基准测试，针对联邦设置中的不同分布退化类型和用户数量进行研究。

盲目图像超分辨率的联邦学习

Federated Learning for Blind Image Super-Resolution

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: this https URL
Datasets:
this https URL

本研究在 14 个小数据集上进行了高倍率 (8x) 的盲图像超分辨率 (DL) 技术的评估，包括 Adaptive Pseudo Augmentation (APA)、Blind Image SR with Spatially Variant Degradations (BlindSR)、 Deep Alternating Network (DAN)、FastGAN 和 Mixture of Experts Super-Resolution (MoESR)，并使用 NIQE 和 MANIQA 评估这些方法的表现。其中，MoESR 被认为是最好的解决方案，但所有技术创建的 HR 图像的感知质量仍需要改进。