Deep learning has garnered significant attention in fluid turbulence modeling and analysis. One such application involves using super-resolution (SR) algorithms to reconstruct small-scale structures from their larger counterparts in turbulent flows. Current SR algorithms are limited by the requirement of supervised training or unpaired high-resolution reference data, making them difficult to implement for practical fluid flow scenarios. Consequently, the development of physics-guided models that can take advantage of the multi-scale nature of turbulence becomes crucial. To address these challenges, we present a physics-guided self-supervised workflow based on deep neural networks for reconstructing small-scale structures in homogeneous isotropic turbulence. Through evaluation using various statistical metrics like spectra, structure functions, and probability density functions, we demonstrate the quality of the reconstruction, showing promising agreement with the ground truth data, even though the latter was not included during training. Our work opens up possibilities for reconstructing small-scale structures from large-eddy simulation data, providing prospects for further advances in this field.