SRTGAN: Triplet Loss Based Generative Adversarial Network for Real-World Super-Resolution

EasyChair Preprint no. 7807, version history

VersionDatePagesVersion notes
1April 18, 20229
2October 24, 202215

Camera-Ready version of the paper accepted at the Computer Vision and Image Processing Conference (CVIP) 2022.

The revised version includes minor changes such as:

  • paper formatted to 15 pages (according to conference format)
  • Acronyms for PSNR, LPIPS, SSIM, etc. are defined.
  • Fig. 2 caption changed to training framework rather than proposed architecture for better understanding.
  • Improved the sentence "The first GAN-based framework
    called SRGAN [14] introduced.. " such that it was the first to introduce perceptual loss, in addition to using the content and adversarial loss.

Keyphrases: deep learning, Generative Adversarial Networks, image super-resolution

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Dhruv Patel and Abhinav Jain and Simran Bawkar and Manav Khorasiya and Kalpesh Prajapati and Kishor Upla and Kiran Raja and R. Raghavendra and Christoph Busch},
  title = {SRTGAN: Triplet Loss Based Generative Adversarial Network for Real-World Super-Resolution},
  howpublished = {EasyChair Preprint no. 7807},

  year = {EasyChair, 2022}}