FMEN

Lowest memory consumption and second shortest runtime in NTIRE 2022 on Efficient Super-Resolution.

Our paper: Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution .

Main Contribution

1. Enhanced Residual Block.
2. High-Frequency Attention Block.
3. Batch Normalization layers can be applied to attention branch to boost performance.

Train

Our goal is to design a strightforward but powerful backbone for lightweight image super-resolution, so the testing model topology is really simple (only contains five highly optimized operators: 3x3 convolution, LeakyReLU, element-wise addition, element-wise multiplication and sigmoid).

Since there are no other tricks, you can directly adopt EDSR framework to train the model.