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cszn / BSRGAN

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (ICCV, 2021) (PyTorch) - We released the training code!

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Replication of BSRGAN results

Magauiya opened this issue · comments

commented

Dear authors,

I am more than interested in your work and would very much appreciate if you answer the following questions:

  • Have you used the blindsr.degradation_bsrgan script to generate test dataset? I inferenced BSRNet and RRDB from type-I to type-3 (of proposed DIV2K4D) and achieved almost the same results as reported in the paper. However, results on type-4 (generated by abovementioned script) led to significantly worse results: BSRNet 24.40 +- 0.14 dB (over 10 realizations) and RRDB: 22.50 +- 0.14 dB. Metrics are calculated in Y channel.
  • Do you use the same script to generate a training dataset? Also, are the training parameters (learning rate, patch shape (72x72), num. of iterations, etc.) of BSRNet the same as BSRGAN?

Sincerely,
Magauiya