## 4 Experiments
### Ablation Study  
In this paper, we mainly used our deblurring pipeline and multi-scale classifier for the automatic pollen grain classification task. In order to demonstrate the effectiveness of each aspect clearly, we conducted ablation experiments to illustrate the contributions of each process.  
To verify the effectiveness of the deblurring pipeline, we tested the classification performance of the deblurring pipeline using single-scale classifiers. Results were recorded for the \"Single-Scale\" and \"Single-Scale + Deblur\" processes. \"Single-Scale\" means that a single-scale classifier was used, which was based on the VGG-19 or ResNet-50 backbones, and \"Single-Scale + Deblur\" represents the single-scale classifier being used with the deblurring pipeline. The image resolutions of the results from the two processes are shown in Figure 9a,b. Thus, we set the normalized resolution to 112 \(\times\) 112 for the \"Single-Scale\" process. For \"Single-Scale + Deblur\", all of the images in our proposed dataset were deblurred using SRGAN and the normalized resolution was set to 448 \(\times\) 448.  
To verify the effectiveness of our multi-scale classifier, we compared the results from the \"Single-Scale + Selector + Deblur\" and \"Multi-Scale + Selector + Deblur\" processes. The normalized resolution was set to 336 \(\times\) 336 for the single-scale classifier, while the settings for the multi-scale classifier were those that were introduced in Section 4.1. All of the samples in our proposed dataset were processed using our selector addition and deblurring pipeline, the resolution range of which is shown in Figure 9c. It can be seen that the image resolutions in Figure 9a were concentrated under 100 \(\times\) 100 and that the image resolutions in Figure 9b were concentrated under 400 \(\times\) 400. Meanwhile, the resolution distribution of samples in the two classifiers was consistent. However, the resolution distribution in Figure 9c could be divided into two resolution ranges, with each range covering the samples of all of the pollen categories, which was more suitable for training the multi-scale classifier. The samples in Figure 9c not only had a higher image quality but could also ensure the feature learning of all of the categories in each branch.  
Figure 8: The confusion matrices for the “Multi-Scale + Selector + Deblur” process with the VGG-19 and Resnet-50 backbones: (**a**) the confusion matrix of the multi-scale classifier with the VGG-19 backbone; (**b**) the confusion matrix of the multi-scale classifier with the Resnet-50 backbone. The lighter blue colors represent lower values and darker blue colors represent higher values. The columns represent the true pollen labels while the rows represent the predicted labels that were classified by the multi-scale classifier.  
Table 7 shows the classification results from the four cases, with the two groups of ablation experiments for each of the VGG-19 and Resnet-50 backbones. In the first group of ablation experiments, it was obvious that the accuracy and F1-score of the \"Single-Scale + Deblur\" process was higher than that of the \"Single-Scale\" process. The \"Single-Scale + Deblur\" process with the Resnet-50 backbone achieved an accuracy of 0.962 and an F1-score of 0.941, which proved that using SRGAN to deblur images was effective for improving the classification performance. Secondly, the performance of the multi-scale classifier was generally better than that of the single-scale classifier. The highest accuracy was 0.977, which was achieved using the two-branch multi-scale classifier with the Resnet-50 backbone. The F1-score for this process was 0.966, which proved that the multi-scale classification structure was effective for improving the performance of pollen image classification.  
Figure 9: The boxplots of the image size/resolution of the samples in the seven categories of the ablation experiments: (**a**) “Single-Scale”; (**b**) “Single-Scale + Deblur”; (**c**) “Single-Scale + Selector + Deblur” and “Multi-Scale + Selector + Deblur”.