Physics‐Guided Multi‐Channel Restoration for Efficient and Accurate Atomic Force Microscopy Imaging

Scientific imaging instruments often acquire multiple physically complementary signals within a single measurement. In atomic force microscopy (AFM), high‐throughput imaging requires higher scan speeds, however, image quality is fundamentally limited by multiple distortions. These artifacts are further amplified for sharp nano/microstructures, making accurate image restoration inherently challenging. Most deep learning‐based approaches rely on a single primary channel and improve performance by increasing model complexity or training data. Here, we introduce a physics‐guided multi‐channel input approach for AFM that utilizes co‐acquired auxiliary signals to improve image restoration efficiency and accuracy. We construct a four co‐acquired channel input, each containing complementary physical information. By integrating these channels into a lightweight convolutional neural network, the proposed approach significantly improves restoration quality with minimal additional parameters. At a scan rate of 7.0 Hz, the four‐channel DnCNN achieved higher reconstruction accuracy than a single‐channel Restormer with approximately 47 times as many parameters, while providing approximately 24 times faster inference. Across the evaluated EFM and PFM conditions, four‐channel inputs reduced the training‐patch requirement by approximately two to three times at matched PSNR.