PDF build & README.md update

README.md

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 # Enhancing Images with Uneven Illumination using Ensemble Learning (EIUIE)
 This repository contains the codebase for the EIUIE approach to enhancing images with uneven illumination.
 
+## Usage
+To enhance a single image, the following cammand can be used:
+
+```bash
+python eiuie/main.py single --file=<PATH_TO_FILE> --method=unsharp_masking
+python eiuie/main.py single --file=<PATH_TO_FILE> --method=retinex
+python eiuie/main.py single --file=<PATH_TO_FILE> --method=homomorphic_filtering
+python eiuie/main.py single --file=<PATH_TO_FILE> --method=fusion
+```
+
+In order to use the fusion model, its parameters have to be trained first.
+
+## Training
+First, the [LOL-dataset](https://www.kaggle.com/datasets/soumikrakshit/lol-dataset) has to be downloaded, extracted, and moved to `data/lol_dataset/`.
+
+From this dataset training samples will be generated. This happens in two steps:
+In the first step, the three base methods (unsharp_masking, retinex, homomorphic_filtering) are applied to all images in the LOL-dataset. Results will be stored in `data/intermediate_images/`
+```bash
+python eiuie/main.py batch_process
+```
+Afterwards, the training dataset consisting of all pixel samples can be generated to `data/pixel_dataset/` using:
+```bash
+python eiuie/main.py prepare_dataset
+```
+
+Training can then be conducted, with the best model parameters (`best_model.pt`) as well as further checkpoints being saved to `data/checkpoints/`, using:
+```bash
+python eiuie/main.py train
+```
+
+The learnt parameters can be pretty printed using:
+```bash
+python eiuie/main.py ppparams
+```
+
+Finally, training can be evaluated on a custon evaluation dataset (`data/eval_dataset/input/`) with the following command. Output images and scores for different evaluation metrics can be found afterwards in `data/eval_dataset`.
+```bash
+python eiuie/main.py eval
+```
+