single image deraining using U-Net on the Rain100L and Rain100H datasets
Single image deraining is a challenging task in computer vision that aims to remove rain streaks from images while preserving important details. U-Net, a popular architecture for image segmentation tasks, can also be adapted for this purpose. Below is a general guide on how to implement single image deraining using a U-Net model on the Rain100L and Rain100H datasets.

### Step-by-Step Guide

#### 1. **Dataset Preparation**
   - **Download the Datasets**: Obtain the Rain100L and Rain100H datasets, which contain rainy images and their corresponding clean images.
   - **Data Organization**: Organize the datasets into training and validation folders. For example:
     ```
     /data/
       /Rain100L/
         /rain/
         /clear/
       /Rain100H/
         /rain/
         /clear/
     ```

#### 2. **Install Required Libraries**
   Ensure you have the necessary libraries installed. You can use Python with TensorFlow or PyTorch. Here’s an example with PyTorch:
   ```bash
   pip install torch torchvision matplotlib
   ```

#### 3. **U-Net Model Implementation**
   Here’s a simple implementation of the U-Net model in PyTorch:

   ```python
   import torch
   import torch.nn as nn

   class UNet(nn.Module):
       def __init__(self, in_channels, out_channels):
           super(UNet, self).__init__()
           self.encoder1 =
import numpy as np
import matplotlib.pyplot as plt
from deepmodels.pretrained_models import UNet

# Load the datasets
data_path = "datasets/Rain100L"
if not np.exist(data_path + "/images"):
    print("Downloading Rain100L dataset")
    from urllib.request import urllib2
    from bs4 import BeautifulSoup
    urllib2.install_opener()
    page = urllib2.urlopen("http://www.eecs.qmul.ac.uk/~mmk/rain/")
    soup = BeautifulSoup(page.read())
    images = soup.find_all("img", class_="img_wrap")
    for image in images:
        url = image.get("src")
        name = image.get("alt")
        print("Downloaded %s" % name)
        np.save(data_path + "/" + name, url)

data_path_h = "datasets/Rain100H"
if not np.exist(data_path_h + "/images"):
    print("Downloading Rain100H dataset")
    from urllib.request import urllib2
    from bs4 import BeautifulSoup
    urllib2