  Following recent advancements in multimode fiber (MMF), miniaturization of
imaging endoscopes has proven crucial for minimally invasive surgery in vivo.
Recent progress enabled by super-resolution imaging methods with a data-driven
deep learning (DL) framework has balanced the relationship between the core
size and resolution. However, most of the DL approaches lack attention to the
physical properties of the speckle, which is crucial for reconciling the
relationship between the magnification of super-resolution imaging and the
quality of reconstruction quality. In the paper, we find that the
interferometric process of speckle formation is an essential basis for creating
DL models with super-resolution imaging. It physically realizes the upsampling
of low-resolution (LR) images and enhances the perceptual capabilities of the
models. The finding experimentally validates the role played by the physical
upsampling of speckle-driven, effectively complementing the lack of information
in data-driven. Experimentally, we break the restriction of the poor
reconstruction quality at great magnification by inputting the same size of the
speckle with the size of the high-resolution (HR) image to the model. The
guidance of our research for endoscopic imaging may accelerate the further
development of minimally invasive surgery.
