Convolutional neural network-based registration for mosaicing of microscopic images

Abstract. To obtain wider field of view through optical microscopes, we proposed an image mosaicing method based on the convolutional neural network (CNN). The CNN was designed to discriminate the corresponding patches in a pair of images. To train the CNN, a training set that contained image patches and their matching labels was created from an open-access database. The pretrained CNN was then fine-tuned by self-learning from the target image and its transformed images. With the proposed self-learning CNN, the corresponding feature points were detected for the registration in mosaicing. The proposed method was compared with the scale-invariant feature transform detector-based and speed-up robust feature detector-based methods for mosaicing of 30 pairs of microscopic images. The proposed method outperformed the two traditional methods in terms of both visual quality and objective assessment. Results demonstrate that using the self-learning CNN can improve the accuracy of registration in image mosaicing.