Transformer for Burst Image Super-Resolution (TBSR)

This is the official PyTorch implementation of TBSR.

Our team received 2nd place (real data track) and 3rd place (synthetic track) in NTIRE 2022 Burst Super-Resolution Challenge (CVPRW 2022).

Please see more detailed descriptions of TBSR from TBSR.pdf.

1. Framework

Overview of the network architecture for TBSR.

2. Preparation

  • Prerequisites

    • Python 3.x and PyTorch 1.6.
    • OpenCV, NumPy, Pillow, CuPy, tqdm, lpips, scikit-image and tensorboardX.
  • Dataset

3. Quick Start

  • Training

  • Testing

    • The pre-trained models can be downloaded. You need to put the two folders in the ./ckpt/ folder.

    • For track 1, modify dataroot in sh and then run:


    • For track 2, modify dataroot in sh and then run:


  • Note

    • You can specify which GPU to use by --gpu_ids, e.g., --gpu_ids 0,1, --gpu_ids 3, --gpu_ids -1 (for CPU mode). In the default setting, all GPUs are used.
    • You can refer to options for more arguments.


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