readme

README.md

@@ -49,14 +49,12 @@ Download ICVL hyperspectral image database from [here](http:https://icvl.cs.bgu.ac.il/
 * Download our pretrained models from [OneDrive](https://1drv.ms/u/s!AqddfvhavTRiijWftKWgLfUgdSaD?e=nHGjIk) and move them to ```checkpoints/qrnn3d/gauss/``` and ```checkpoints/qrnn3d/complex/``` respectively.
 
 * [Blind Gaussian noise removal]: 
-```python hsi_eval.py -a qrnn3d -p gauss -r -rp checkpoints/qrnn3d/gauss/model_epoch_50_118454.pth```
 ```python hsi_test.py -a qrnn3d -p gauss -r -rp checkpoints/qrnn3d/gauss/model_epoch_50_118454.pth```
 
 * [Mixture noise removal]: 
-```python hsi_eval.py -a qrnn3d -p complex -r -rp checkpoints/qrnn3d/complex/model_epoch_100_159904.pth``` 
 ```python hsi_test.py -a qrnn3d -p complex -r -rp checkpoints/qrnn3d/complex/model_epoch_100_159904.pth```
 
-Note ```hsi_eval.py``` is for evaluation purpose (w. ground truth provided), while ```hsi_test``` is for testing purpose (w.o. GT)
+You can also use ```hsi_eval.py``` to evaluate quantitative HSI denoising performance. 
 
 ### 3. Training from scratch