RCAN model is a super-resolution network. The corresponding high-resolution image is reconstructed from the low-resolution image. Based on the original image, the length and width are enlarged by two times. It has important application value in the fields of monitoring equipment, satellite images, and medical imaging. The following images show the result of RCAN. The image is still clear after zooming in.
Figure 1. Production Recognition Example
The following table lists the RCAN super-resolution models supported by the Vitis AI Library.
No | Model Name | Framework |
---|---|---|
1 | rcan_pruned_tf | TensorFlow |
2 | ofa-rcan_pt | PyTorch |
3 | drunet_pt | |
4 | SESR_S_pt | |
5 | ofa_rcan_latency_pt | |
6 | xilinxSR_pt |