Super Resolution of Images Using Residual Network

Shreyas Y J, Mallikarjun Honnalli, Sinchana S, Uday Kulkarni, Shashank Hegde

2025

Abstract

Single Image Super Resolution (SISR) is a vital task in computer vision that reconstructs High-Resolution (HR) images from Low-Resolution (LR) inputs. It is widely used in fields like diagnostic imaging, geospatial imaging, and video streaming. In this study, we introduce a Residual Network (ResNet) approach for super-resolution, which addresses challenges like vanishing gradients and captures finer details through deeper architectures. Our ResNet model effectively reduces computational overhead while preserving critical features, ensuring scalability across various image datasets. We evaluated its performance on the DIV2K dataset using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), achieving a PSNR of 30.25 dB and SSIM of 0.77. These results demonstrate that our model outperforms traditional methods and competing architectures, making it a robust solution for applications requiring high precision, such as video enhancement and real-time imaging.

Paper Citation

in Harvard Style

Y J S., Honnalli M., S S., Kulkarni U. and Hegde S. (2025). Super Resolution of Images Using Residual Network. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 864-870. DOI: 10.5220/0013734100004664

in Bibtex Style

@conference{incoft25,
author={Shreyas Y J and Mallikarjun Honnalli and Sinchana S and Uday Kulkarni and Shashank Hegde},
title={Super Resolution of Images Using Residual Network},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={864-870},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013734100004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Super Resolution of Images Using Residual Network
SN - 978-989-758-763-4
AU - Y J S.
AU - Honnalli M.
AU - S S.
AU - Kulkarni U.
AU - Hegde S.
PY - 2025
SP - 864
EP - 870
DO - 10.5220/0013734100004664
PB - SciTePress