Artificial Intelligence and Communication Technologies
Comparative Analysis of Deep Convolution Networks Based Image Super-Resolution Techniques
Authors: Renuka Sindge, Maitreyee Dutta and Jagriti Saini
Publishing Date: 10-04-2023
ISBN: 978-81-955020-5-9
Abstract
Single Image Super-Resolution (SISR) is gaining huge attention in the digital age across various application domains such as surveillance, medical imaging, and agriculture. Numerous SR methods based on deep learning were used by existing researchers to improve image resolution. Literature shows that deep convolutional neural networks (CNNs) perform exceptionally well to handle degraded images. In this study, CNN-based methods from a deep learning environment are compared to reconstruct the High-Resolution (HR) images. Observations show that SRCNN and FSRCNN can achieve considerable image quality after reconstruction; however, performance is limited to small datasets due to shallow network parameters. Furthermore, VDSR and LapSRN were also utilized against heavy datasets due to their huge computational efficiency.
Keywords
Image super-resolution, Single image super-resolution, Convolutional neural network, Deep Learning.
Cite as
Renuka Sindge, Maitreyee Dutta and Jagriti Saini, "Comparative Analysis of Deep Convolution Networks Based Image Super-Resolution Techniques", In: Saroj Hiranwal and Garima Mathur (eds), Artificial Intelligence and Communication Technologies, SCRS, India, 2023, pp. 1009-1016. https://doi.org/10.52458/978-81-955020-5-9-96