DARA-Net: a dual-path residual attention network for denoising AFM images under complex noise conditions

Atomic force microscopy (AFM) images often suffer from random fluctuations, periodic interference, and scanning artifacts. We propose DARA-Net, a denoising framework combining a dual U-shaped GAN architecture with residual attention blocks (RABs) in the decoder stage. Two parallel predictors-one for structural content and one for noise-enable effective feature disentanglement, while the GAN framework enhances detail realism. The fused output benefits from the RAB's ability to suppress complex noise and preserve nanoscale topographical features. A dataset of 1000 in-house AFM images with synthetic degradation was used for training and evaluation. The results show that DARA-Net outperforms classical and state-of-the-art methods in PSNR, SSIM, and RMSE and achieves lower errors in four physical surface metrics (perimeter, height, roughness, and volume), demonstrating superior generalization and structural preservation for nanoscale imaging.