Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data. The most common failure modes are the biases and errors produced by the localisation algorithm when there is emitter overlap. Also known as the high density or crowded field condition, significant emitter overlap is normally unavoidable in live cell imaging. Here we use Haar wavelet kernel analysis (HAWK), a localisation microscopy data analysis method which is known to produce results without bias, to generate a reference image. This enables mapping and quantification of reconstruction bias and artefacts common in all but low emitter density data. By avoiding comparisons involving intensity information, we can map structural artefacts in a way that is not adversely influenced by nonlinearity in the localisation algorithm. The HAWK Method for the Assessment of Nanoscopy (HAWKMAN) is a general approach which allows for the reliability of localisation information to be assessed.

由于难以可靠地检测实验数据中的错误，因此评估定位显微镜图像的质量非常具有挑战性。最常见的故障模式是当存在发射器重叠时定位算法产生的偏差和错误。也称为高密度或拥挤的场条件，显着的发射器重叠在活细胞成像中通常是不可避免的。在这里，我们使用 Haar 小波核分析 (HAWK)，一种已知可以产生无偏差结果的定位显微镜数据分析方法，以生成参考图像。这可以对除低发射器密度数据之外的所有数据中常见的重建偏差和伪影进行映射和量化。通过避免涉及强度信息的比较，我们可以以一种不受定位算法中的非线性不利影响的方式映射结构伪影。用于评估纳米镜的 HAWK 方法 (HAWKMAN) 是一种通用方法，它允许评估定位信息的可靠性。