Single-image super-resolution methods based on improved convolutional neural networks

Rongfu Wang; Mary Jane C. Samonte

doi:10.1117/12.3038073

7 August 2024 Single-image super-resolution methods based on improved convolutional neural networks

Rongfu Wang, Mary Jane C. Samonte

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132292S (2024) https://doi.org/10.1117/12.3038073
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

This paper introduces a single-image super-resolution method based on improved convolutional neural networks. This method aims to improve the spatial resolution of images by designing new network architectures, employing multi-scale loss functions and data enhancement techniques. Experimental results show that the proposed method achieves significantly better results than conventional methods and state-of-the-art SRCNN-based methods on multiple super-resolution datasets, which can better recover image details and generate clear and realistic high-resolution images. The study in this paper is essential for single-image super-resolution. It provides a reference for future improvements in network architectures and training strategies and explores the application potential of other deep learning techniques.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rongfu Wang and Mary Jane C. Samonte "Single-image super-resolution methods based on improved convolutional neural networks", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132292S (7 August 2024); https://doi.org/10.1117/12.3038073

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KEYWORDS

Education and training

Super resolution

Design

Image restoration

Convolutional neural networks

Data modeling

Performance modeling

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