To resolve the conflict between global structure modeling and local detail preservation in image super-resolution, we propose SwinT-SRGAN, a novel framework integrating Swin Transformer with GAN. Key innovations include: (1) A dual-path generator where Transformer captures long-range dependencies via window attention while CNN extracts high-frequency textures; (2) An end-to-end Detail Recovery Block (DRB) suppressing artifacts through dual-path attention; (3) A triple-branch discriminator enabling hierarchical adversarial supervision; (4) A dynamic loss scheduler adaptively balancing six loss components (pixel/perceptual/high-frequency constraints). Experiments on CelebA-HQ and Flickr2K demonstrate: (1) Very good performance (max gains: 0.71 dB PSNR, 0.83% SSIM, 4.67 LPIPS reduction vs. Swin-IR); (2) Ablation studies validate critical roles of DRB. This work offers a robust solution for high-frequency-sensitive applications.