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

arXiv: https://arxiv.org/abs/1809.00961 · HTML: https://mlsanity.com/paper/1809.00961/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images](https://mlsanity.com/paper/2104.04926/) (2021)
- [Single Image Super-Resolution Based on Capsule Neural Networks](https://mlsanity.com/paper/2210.03743/) (2022)
- [Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing](https://mlsanity.com/paper/2412.18165/) (2024)
- [Sim2Real SAR Image Restoration: Metadata-Driven Models for Joint Despeckling and Sidelobes Reduction](https://mlsanity.com/paper/2601.01541/) (2026)

## Abstract (arXiv)

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.