MSCE: An Edge-Preserving Robust Loss Function for Improving Super-Resolution Algorithms

  • Ram Krishna Pandey
  • Nabagata Saha
  • Samarjit Karmakar
  • A. G. Ramakrishnan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


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.


Loss function CNN GAN Super-resolution Mean square error Mean square Canny error Edge preservation PSNR SSIM 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ram Krishna Pandey
    • 1
  • Nabagata Saha
    • 2
  • Samarjit Karmakar
    • 2
  • A. G. Ramakrishnan
    • 1
  1. 1.Department of Electrical EngineeringIndian Institute of ScienceBangaloreIndia
  2. 2.Department of Computer Science and EngineeringNIT WarangalWarangalIndia

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