Image super-resolution from low-resolution fundus image has valuable applications in clinical practices. The popular methods yield unsatisfactory results when the fundus images are contaminated due to the bleeding or plaques caused by eye diseases. To this end, we propose a style-guided UNet (SGUNet) which incorporates a series of style-guided U-shape block (SUB) for fundus image super-resolution. Each SUB consists of trunk and mask branches. The proposed trunk branch is a U-shape structure that intends to enlarge the receptive field by down-sampling via large-stride convolution, and fuses the complementary information under the different receptive fields. The mask branch then dynamically estimates the relative importance of individual potential styles to reweigh the feature maps according to the significance of the potential styles. To fully leverage the hierarchical features, a dense feature fusion scheme is introduced by concatenating the output of preceding SUBs. We extensively validate the proposed network on low-resolution retina dataset with adversely affected by diseases. The experimental results demonstrate that our SGUNet achieves superior performance with excellent robustness and high accuracy by comparing with six popular methods.

来自低分辨率眼底图像的图像超分辨率在临床实践中具有有价值的应用。当眼底图像由于眼病引起的出血或斑块而被污染时，流行的方法会产生不令人满意的结果。为此，我们提出了一种风格引导的 UNet（SGUNet），它结合了一系列风格引导的 U 形块（SUB）用于眼底图像超分辨率。每个SUB由主干和掩码分支组成。所提出的主干分支是一个 U 形结构，旨在通过大步幅卷积进行下采样来扩大感受野，并融合不同感受野下的互补信息。然后，掩码分支动态估计各个潜在风格的相对重要性，以根据潜在风格的重要性重新加权特征图。为了充分利用分层特征，通过连接前面 SUB 的输出引入了密集特征融合方案。我们在受疾病不利影响的低分辨率视网膜数据集上广泛验证了提议的网络。实验结果表明，与六种流行的方法相比，我们的 SGUNet 实现了卓越的性能、出色的鲁棒性和高精度。