Effects Of Normalised SSIM Loss On Super-Resolution Tasks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F25%3A101814" target="_blank" >RIV/60460709:41110/25:101814 - isvavai.cz</a>
Result on the web
<a href="https://www.techscience.com/CMES/v143n3/62840" target="_blank" >https://www.techscience.com/CMES/v143n3/62840</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.32604/cmes.2025.066025" target="_blank" >10.32604/cmes.2025.066025</a>
Alternative languages
Result language
angličtina
Original language name
Effects Of Normalised SSIM Loss On Super-Resolution Tasks
Original language description
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 L_SSIM_N, 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 L_SSIM_N significantly contributes to the visual quality, preserving the structural fidelity on the reference datasets. The quantitative analysis of results while incorporating L_SSIM_N 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
CMES - Computer Modeling in Engineering and Sciences
ISSN
1526-1492
e-ISSN
1526-1492
Volume of the periodical
143
Issue of the periodical within the volume
3
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
21
Pages from-to
3329-3349
UT code for WoS article
001527987300001
EID of the result in the Scopus database
2-s2.0-105010567210