Structure-preserving super-resolution of retinal fundus images via a dual-transformer residual network

High-resolution retinal fundus images are critical for diagnosing diabetic retinopathy, yet clinical datasets often contain low-resolution images that obscure fine vascular structures essential for accurate diagnosis. Existing super-resolution methods face a fundamental trade-off: convolutional neural networks produce overly smooth results, while generative adversarial networks (GANs) risk creating hallucinated artifacts. We propose the Dual-Transformer Residual Super-Resolution Network (DTRSRN), a hybrid architecture combining Swin Transformers for global context modeling with a parallel residual Convolutional Neural Network (CNN) pathway for fine-grained vascular detail preservation. Our key innovation lies in using Fractal Dimension analysis to quantitatively measure retinal vascular morphology preservation. Experimental results on three benchmark datasets demonstrate that DTRSRN achieves 33.64 dB PSNR for 2 × super-resolution, out performing state-of-the-art methods including SwinIR (+0.96 dB), HAT (+0.37 dB), and ResShift (+0.30 dB). Critically, DTRSRN achieves 17.0% improvement in vascular structure preservation (Δ<i>D</i> _<i>f</i> = 0.0987) compared to the best baseline, demonstrating superior clinical relevance for retinal image enhancement.