ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

MSCE: An edge-preserving robust loss function for improving super-resolution algorithms

Pandey, RK and Saha, N and Karmakar, S and Ramakrishnan, AG (2018) MSCE: An edge-preserving robust loss function for improving super-resolution algorithms. In: 25th International Conference on Neural Information Processing, ICONIP 2018, 13 - 16 December 2018, Siem Reap, pp. 566-575.

Full text not available from this repository.
Official URL: https://doi.org/10.1007/978-3-030-04224-0_49


With the recent advancement in the deep learning technologies such as CNNs and GANs, there is significant improvement in the quality of the images reconstructed by deep learning based super-resolution (SR) techniques. In this work, we propose a robust loss function based on the preservation of edges obtained by the Canny operator. This loss function, when combined with the existing loss function such as mean square error (MSE), gives better SR reconstruction measured in terms of PSNR and SSIM. Our proposed loss function guarantees improved performance on any existing algorithm using MSE loss function, without any increase in the computational complexity during testing.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Verlag
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Deep learning; Errors; Image enhancement; Image reconstruction; Optical resolving power, Edge preservations; Loss functions; Mean square; PSNR; SSIM; Super resolution, Mean square error
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 02 Sep 2022 04:05
Last Modified: 02 Sep 2022 04:05
URI: https://eprints.iisc.ac.in/id/eprint/76353

Actions (login required)

View Item View Item