  Gravitational lensing data is frequently collected at low resolution due to
instrumental limitations and observing conditions. Machine learning-based
super-resolution techniques offer a method to enhance the resolution of these
images, enabling more precise measurements of lensing effects and a better
understanding of the matter distribution in the lensing system. This
enhancement can significantly improve our knowledge of the distribution of mass
within the lensing galaxy and its environment, as well as the properties of the
background source being lensed. Traditional super-resolution techniques
typically learn a mapping function from lower-resolution to higher-resolution
samples. However, these methods are often constrained by their dependence on
optimizing a fixed distance function, which can result in the loss of intricate
details crucial for astrophysical analysis. In this work, we introduce
$\texttt{DiffLense}$, a novel super-resolution pipeline based on a conditional
diffusion model specifically designed to enhance the resolution of
gravitational lensing images obtained from the Hyper Suprime-Cam Subaru
Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging
the detailed structural information present in Hubble Space Telescope (HST)
counterparts. The diffusion model, trained to generate HST data, is conditioned
on HSC data pre-processed with denoising techniques and thresholding to
significantly reduce noise and background interference. This process leads to a
more distinct and less overlapping conditional distribution during the model's
training phase. We demonstrate that $\texttt{DiffLense}$ outperforms existing
state-of-the-art single-image super-resolution techniques, particularly in
retaining the fine details necessary for astrophysical analyses.
