Aiming at the problems of high difficulty and high cost in acquiring existing high-quality paired datasets for fluorescence microscopy and the artifacts easily generated by image super-resolution at low fluorescence levels, this paper proposes an unsupervised method based on super-resolution reconstruction of fluorescence microscopy images, which realizes pre-training-free image super-resolution by incorporating this paper's innovative sparsity extraction module and attention gates into encoder-decoder network model, and obtains reconstruction more consistent with human senses. by incorporating the innovative sparsity extraction module and attention gate into the encoder-decoder network model in this paper, to realize pre-training-free image super-resolution reconstruction and to obtain reconstruction more in line with human senses. Experiments are conducted on three different cellular structure datasets from the publicly available dataset BioSR to evaluate the method approach of this paper. Taking the first signal-to-noise level dataset of Microtubules as an example, the method improves 7 dB on PSNR, 0.21 on SSIM and reduces 4.6 on NIQE compared to the classical unsupervised algorithm DIP. Overall, the present method is more suitable for super-resolution tasks in fluorescence microscopy.