Learning from hybrid bright-dark field imaging for resolution-enhanced digital pathology

High-throughput whole-slide imaging (WSI) confronts a trade-off between scanning speed and optical resolution, rendering low-numerical-aperture objectives inadequate for resolving critical microstructures. Computational resolution enhancement offers an alternative; however, the ill-posedness of single-image resolution enhancement leads to over-smoothed reconstructions or hallucinations, undermining clinical reliability. We present HBDF-RE, a hybrid bright-field and dark-field resolution enhancement framework that exploits complementary physical contrast to constrain the reconstruction problem. A single dark-field acquisition, enriched with high-frequency scattering information inaccessible to conventional bright-field imaging, serves as a reference that reduces the solution space and guides reconstruction from low-resolution bright-field inputs. HBDF-RE integrates multimodal feature fusion, spatial attention, and joint spatial-frequency domain optimization to preserve global tissue morphology while recovering fine textural details. Experimental results demonstrate a 2.1× enhancement in resolution (from 950 nm to 445 nm), a 3.2 dB improvement in PSNR (Peak Signal-to-Noise Ratio), and an 84% reduction in reconstruction artifacts compared with representative single-image resolution enhancement methods. Importantly, in large-scale AI-assisted cervical cancer screening, HBDF-RE-reconstructed images yield an 11.14% improvement in diagnostic sensitivity, with pronounced gains in clinically ambiguous lesion categories. These results establish HBDF-RE as a physically grounded and clinically reliable resolution enhancement paradigm for digital pathology, offering a pathway toward next-generation WSI and computer-aided diagnosis.