Super-resolution fluorescence microscopy, with a spatial resolution beyond the diffraction limit of light, has become an indispensable tool to observe subcellular structures at a nanoscale level. To verify that the super-resolution images reflect the underlying structures of samples, the development of robust and reliable artifact detection methods has received widespread attention. However, the existing artifact detection methods are prone to report false alert artifacts because it relies on absolute intensity mismatch between the wide-field image and resolution rescaled super-resolution image. To solve this problem, we proposed DETECTOR, a structural information-guided artifact detection method for super-resolution images. It detects artifacts by computing the structural dissimilarity between the wide-field image and the resolution rescaled super-resolution image. To focus on structural similarity, we introduce a weight mask to weaken the influence of strong autofluorescence background and proposed a structural similarity index for super-resolution images, named MASK-SSIM. Simulations and experimental results demonstrated that compared with the state-of-the-art methods, DETECTOR has advantages in detecting structural artifacts in super-resolution images. It is especially suitable for wide-field images with strong autofluorescence background and super-resolution images of single molecule localization microscopy (SMLM). DETECTOR has extreme sensitivity to the weak signal region. Moreover, DETECTOR can guide data collection and parameter tuning during image reconstruction.

中文翻译：

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DETECTOR：超分辨率荧光显微图像的结构信息引导伪影检测

超分辨率荧光显微镜的空间分辨率超出了光的衍射极限，已成为在纳米级观察亚细胞结构不可或缺的工具。为了验证超分辨率图像反映了样品的底层结构，稳健可靠的伪影检测方法的开发受到了广泛关注。然而，现有的伪影检测方法容易报告误报伪影，因为它依赖于宽视场图像和分辨率重新缩放的超分辨率图像之间的绝对强度失配。为了解决这个问题，我们提出了 DETECTOR，一种结构信息引导的超分辨率图像伪影检测方法。它通过计算宽视场图像和分辨率重新缩放的超分辨率图像之间的结构差异来检测伪影。为了关注结构相似性，我们引入了一个权重掩码来削弱强自发荧光背景的影响，并提出了一种超分辨率图像的结构相似性指数，称为 MASK-SSIM。仿真和实验结果表明，与最先进的方法相比，DETECTOR 在检测超分辨率图像中的结构伪影方面具有优势。它特别适用于具有强自发荧光背景的宽视场图像和单分子定位显微镜（SMLM）的超分辨率图像。DETECTOR 对弱信号区域具有极高的灵敏度。而且，