Add hyperlinks.

Author: ebezzam

configs/train/README.md

@@ -6,8 +6,13 @@ The core PyTorch-based training script can be found [here](https://github.com/LC
 
 The following datasets are supported (hyperlinks takes to relevant configuration description).
 By default, the model architecture uses five unrolleed iterations of ADMM for camera inversion, and UNetRes models for the pre-processor post-processor, and PSF correction.
+With DiffuserCam, we show how to set different camera inversion methods and neural networks architecture for the processors, which can also be used with other datasets.
 
 - [DiffuserCam](#diffusercam)
+    - [Unrolled ADMM](#unrolled-admm)
+    - [Compensation branch](#compensation-branch)
+    - [Trainable inversion](#trainable-inversion)
+    - [Multi wiener deconvolution network](#multi-wiener-deconvolution-network)
     - [Transformer architecture for pre- and post-processors](#transformer-architecture-for-pre--and-post-processors)
     - [Multi PSF camera inversion (PhoCoLens)](#multi-psf-camera-inversion)
 - [TapeCam](#tapecam)
@@ -22,8 +27,6 @@ The configuration files are based on [Hydra](https://hydra.cc/docs/intro/), whic
 
 The output of training can be visualized on WandB (if you have connected with it when launching the script) and will be saved in the `outputs` directory with the appropriate timestamp.
 
-With DiffuserCam, we show how to set different camera inversion methods and neural networks architecture for the processors, which can also be used with other datasets.
-
 
 ## DiffuserCam