## 1 Introduction
Although the methods that use manual feature extraction have received a great amount of attention, their classification performance can be affected by many factors, such as image quality and descriptor selection. Due to the success of convolutional neural networks (CNNs) in the field of computer vision, pollen image classification methods that are based on deep learning have gradually become mainstream. In [16], [PERSON] et al. proposed an automatic feature learning network for pollen image classification using fine-tuned transfer learning. They achieved an accuracy rate of 94% using a 30-class pollen dataset. In [17], [PERSON] et al. studied two methods for training CNNs using a large-scale pollen image dataset: random initialization and fine-tuned feature learning. Their results showed that the process of fine-tuning a pre-trained CNN achieved an accuracy of 96.24%, which was much higher than that of the random initialization method. In [18], [PERSON] et al. focused on the three-dimensional visual information of pollen grains. Their process of analyzing each sample was recorded in MJPEG video format in order to make use of the 3D information that was presented by several focal planes. They used a training set of 251 videos and their detection results achieved scores of 98.54% for recall and 99.75% for precision. However, these deep learning methods can only perform transfer learning using CNNs. Therefore, they do not focus on the characteristics of pollen images. The shape characteristics of different pollen grains vary greatly, which results in the resolutions of pollen grain images spanning a large range. In [19], [PERSON] et al. studied the influence of pollen image size and proposed a pre-processing algorithm. Their pre-processing algorithm cropped the pollen grains from the original images while keeping a minimum amount of padding around them and maintaining their size, so that all of the images could be reframed into 227 \(\times\) 227 images. However, some pollen grains were too small, which resulted in low image resolutions and unclear details. The algorithm in [19] was ineffective for improving the quality of pollen grain images. These unfavorable factors affect the accuracy of image classification. Therefore, it is not sufficient to train a self-collected pollen image dataset directly using a pre-trained CNN with a single normalized resolution. To solve the above limitations, we studied an automatic classification method for allergenic pollen grains in the Beijing area. The proposed work produced the following main contributions:The construction of a multi-class and large-scale dataset of pollen grain images under a light microscope, which is the first large-scale dataset of pollen images from the Beijing area;
* The proposal of a deblurring pipeline for super-resolution reconstruction based on a GAN, which effectively improved the quality of the images by reconstructing the semantic information of the pollen images;
* The proposal of an easy-to-implement and efficient multi-scale deep learning architecture with a multi-branch network for pollen image classification so that images in different resolution ranges could be trained using the corresponding branches, which significantly improved the classification performance. This paper is organized as follows. In Section 2, we review several previous related works about pollen datasets and introduce the main current automatic classification methods. In Section 3, we describe the specific process for the construction of the large-scale dataset of pollen images from the Beijing area. Then, the super-resolution reconstruction module that enhances the quality of blurred pollen images is proposed. Next, a multi-scale classification method is introduced for the automatic classification of the proposed dataset. The experimental results and ablation study are shown in Section 4 and we present our conclusions and directions for future research in Section 5.