@Article{cmes.2025.066025,
AUTHOR = {Adéla Hamplová, Tomáš Novák, Miroslav Žáček, Jiří Brožek},
TITLE = {Effects of Normalised SSIM Loss on Super-Resolution Tasks},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {143},
YEAR = {2025},
NUMBER = {3},
PAGES = {3329--3349},
URL = {http://www.techscience.com/CMES/v143n3/62840},
ISSN = {1526-1506},
ABSTRACT = {This study proposes a new component of the composite loss function minimised during training of the Super-Resolution (SR) algorithms—the normalised structural similarity index loss , which has the potential to improve the natural appearance of reconstructed images. Deep learning-based super-resolution (SR) algorithms reconstruct high-resolution images from low-resolution inputs, offering a practical means to enhance image quality without requiring superior imaging hardware, which is particularly important in medical applications where diagnostic accuracy is critical. Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity, visual artefacts may persist, making the design of the loss function during training essential for ensuring reliable and naturalistic image reconstruction. Our research shows on two models—SR and Invertible Rescaling Neural Network (IRN)—trained on multiple benchmark datasets that the function  significantly contributes to the visual quality, preserving the structural fidelity on the reference datasets. The quantitative analysis of results while incorporating  shows that including this loss function component has a mean 2.88% impact on the improvement of the final structural similarity of the reconstructed images in the validation set, in comparison to leaving it out and 0.218% in comparison when this component is non-normalised.},
DOI = {10.32604/cmes.2025.066025}
}