Abstract: In the field of clinical medicine, pathological analysis of patient tissue sections is considered the gold standard for assessing complex diseases. Traditional super-resolution methods often fail to effectively capture fine structures and textures in pathological images, leading to suboptimal reconstruction performance. To address this issue, this paper proposes a novel super-resolution generative adversarial network based on a parallel attention mechanism, referred to as PASRGAN (Parallel Attention Super-Resolution Generative Adversarial Network). The proposed algorithm adopts a parallel execution of channel and spatial attention mechanisms to overcome the information dispersion issues inherent in traditional attention mechanisms. Furthermore, a feature grouping and channel shuffle strategy is introduced, which enhances feature diversity while maintaining low computational costs, thereby significantly improving the reconstruction performance of pathological images. Considering that most existing super-resolution studies on pathological images are conducted on simulated datasets, which fail to fully reflect the challenges of real-world image degradation, this paper constructs paired low-resolution and high-resolution image datasets based on the Camelyon16 dataset to validate the proposed algorithm's effectiveness in real-world scenarios. Experimental results demonstrate that, compared to state-of-the-art super-resolution methods (e.g., ESRGAN, CWT-Net, Histo-Diffusion, and URCDM), PASRGAN achieves superior performance with a PSNR of 25.33 dB, SSIM of 0.6659, and perceptual index (PI) of 5.14. In addition, PASRGAN achieves significantly lower parameter complexity (10.8M) and floating-point operations (FLOPs, 48.9G) compared to traditional methods, confirming its computational efficiency. Ablation studies further analyze the contributions of the parallel attention mechanism, the shuffle operation, and the improvements in the generator and discriminator structures, verifying their effectiveness.

摘要： 在临床医学领域，患者组织切片的病理学分析是评估复杂疾病的金标准。传统的超分辨率方法在处理病理图像时，常因无法有效捕捉图像中的细微结构和纹理，导致重建效果不佳。为解决这一问题，提出了一种基于并联注意力机制的超分辨率生成式对抗网络算法PASRGAN(Parallel Attention Super-Resolution Generating Adversarial Network, PASRGAN)。该算法通过通道和空间注意力机制的并联运行，有效解决了传统注意力机制的信息分散问题。此外，本文引入特征分组和通道混乱(shuffle)策略，在保持计算开销低的前提下提升了特征多样性，从而显著提升病理图像的重建性能。鉴于现有的病理图像超分辨率研究大多是在模拟数据集上进行的，无法完全揭示现实图像超分辨率的挑战。因此，本文基于Camelyon16数据集构建了真实低分辨率与高分辨率图像对，验证了算法在病理图像超分任务中的优越性。实验表明，与当前超分辨率算法（如ESRGAN、CWT-Net、Histo-Diffusion和URCDM）相比，PASRGAN在PSNR（25.33dB）、SSIM（0.6659）和感知指标PI（5.14）上均优于其他算法。同时，PASRGAN的参数量（10.8M）和FLOPs（48.9G）显著低于传统方法，验证了其在计算效率上的优势。消融实验进一步分析了并联注意力机制、shuffle操作以及生成器和判别器结构的有效性。