  Histopathology plays a pivotal role in medical diagnostics. In contrast to
preparing permanent sections for histopathology, a time-consuming process,
preparing frozen sections is significantly faster and can be performed during
surgery, where the sample scanning time should be optimized. Super-resolution
techniques allow imaging the sample in lower magnification and sparing scanning
time. In this paper, we present a new approach to super resolution for
histopathological frozen sections, with focus on achieving better distortion
measures, rather than pursuing photorealistic images that may compromise
critical diagnostic information. Our deep-learning architecture focuses on
learning the error between interpolated images and real images, thereby it
generates high-resolution images while preserving critical image details,
reducing the risk of diagnostic misinterpretation. This is done by leveraging
the loss functions in the frequency domain, assigning higher weights to the
reconstruction of complex, high-frequency components. In comparison to existing
methods, we obtained significant improvements in terms of Structural Similarity
Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), as well as indicated
details that lost in the low-resolution frozen-section images, affecting the
pathologist's clinical decisions. Our approach has a great potential in
providing more-rapid frozen-section imaging, with less scanning, while
preserving the high resolution in the imaged sample.
