Abstract

Deep learning plays an important role in the field of machine learning, which has been developed and used in a wide range of areas. Many deep-learning-based methods have been proposed to improve image resolution, most of which are based on image-to-image translation algorithms. The performance of neural networks used to achieve image translation always depends on the feature difference between input and output images. Therefore, these deep-learning-based methods sometimes do not have good performance when the feature differences between low-resolution and high-resolution images are too large. In this paper, we introduce a dual-step neural network algorithm to improve image resolution step by step. Compared with conventional deep-learning methods that use input and output images with huge differences for training, this algorithm learning from input and output images with fewer differences can improve the performance of neural networks. This method was used to reconstruct high-resolution images of fluorescence nanoparticles in cells.

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