用于显微内镜的具有物理激励下采样内核的Zero-shot超分辨率
Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy
摘要：
由于卷积神经网络（CNN）的发展，超分辨率（SR）方法已经取得了重大进展。CNN已经成功地用于提高显微内镜成像的质量。然而，显微内镜中SR研究的固有局限性仍然是缺乏地面实况高分辨率（HR）图像，通常用于监督训练和基于参考的图像质量评估（IQA）。因此，正在探索替代方法，例如无监督SR。为了解决非参考图像质量改善的需求，我们设计了一种新的zero-shot超分辨率（ZSSR）方法，该方法仅依赖于以自监督方式处理的显微内镜数据，而不需要地面真实HR图像。我们量身定制的建议管道的特质，显微内镜通过引入：一个物理动机的Voronoi降尺度内核占显微内镜的不规则的基于纤维的采样模式，和现实的噪声模式。我们还利用视频序列来利用图像序列进行自我监督的zero-shot图像质量改进。我们进行消融研究，以评估我们在降尺度内核和噪声模拟方面的贡献。我们验证我们的方法在合成和原始数据。合成实验进行了评估与参考为基础的IQA，而我们的原始图像的结果进行了评估，在与专家和非专家观察员进行的用户研究。结果表明，ZSSR重建的图像质量优于基线方法。与监督单图像SR相比，ZSSR也具有竞争力，特别是专家首选的重建技术。
英文摘要：
Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of research on SR in endomicroscopy remains the lack of ground truth high-resolution (HR) images, commonly used for both supervised training and reference-based image quality assessment (IQA). Therefore, alternative methods, such as unsupervised SR are being explored. To address the need for non-reference image quality improvement, we designed a novel zero-shot super-resolution (ZSSR) approach that relies only on the endomicroscopy data to be processed in a self-supervised manner without the need for ground-truth HR images. We tailored the proposed pipeline to the idiosyncrasies of endomicroscopy by introducing both: a physically-motivated Voronoi downscaling kernel accounting for the endomicroscope's irregular fibre-based sampling pattern, and realistic noise patterns. We also took advantage of video sequences to exploit a sequence of images for self-supervised zero-shot image quality improvement. We run ablation studies to assess our contribution in regards to the downscaling kernel and noise simulation. We validate our methodology on both synthetic and original data. Synthetic experiments were assessed with reference-based IQA, while our results for original images were evaluated in a user study conducted with both expert and non-expert observers. The results demonstrated superior performance in image quality of ZSSR reconstructions in comparison to the baseline method. The ZSSR is also competitive when compared to supervised single-image SR, especially being the preferred reconstruction technique by experts.
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