Cytopathology Image Super-Resolution of Portable Microscope Based on Convolutional Window-Integration Transformer

High-quality cytopathology images are the guarantee of cervical cancer computer-aided screening. However, obtaining such images is dependent on expensive devices, which hinders the screening popularization in less developed areas. In this study, we propose a convolutional window-integration Transformer for cytopathology image super-resolution (SR) of portable microscope. We use self-attention within the window to integrate patches, and then design a convolutional window-integration feed-forward network with two 5 x 5 size kernels to achieve cross-window patch integration. This design avoids long-range self-attention and facilitates SR local mapping learning. Besides, we design a multi-layer feature fusion in feature extraction to enhance high-frequency details, achieving better SR reconstruction. Finally, we register and establish a dataset of 239,100 paired portable microscope images and standard microscope images based on feature point matching. A series of experiments demonstrate that our model has the minimum parameter number and outperforms state-of-the-art CNN-based and recent Transformer-based SR models with PSNR improvement of 0.09-0.53 dB. We release this dataset and codes publicly to promote the development of computational cytopathology imaging.