# Combined Image Processing Modules: CAT, Deblurring, and Image Translator

## Overview

This repository provides an integrated set of image-processing modules aimed at enhancing image quality and transforming digital sketches into photo-realistic images. The components include the **Contour Accentuation Technique (CAT)**, a **Deblurring Module**, and an **Image Translator Module**, implemented using modern deep learning frameworks like TensorFlow and PyTorch.

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### **1. Contour Accentuation Technique (CAT)**

**Description**: CAT enhances the visibility of image contours using blending and gamma correction techniques. It is lightweight, effective, and suitable for applications like sketch generation, medical imaging, and image analysis.

**Key Features**:
- **Blending**: Converts the input image to grayscale, applies Gaussian blur, and blends it for edge enhancement.
- **Gamma Correction**: Adjusts brightness and contrast to improve contour clarity.
- **Input/Output**: Processes input images to generate contour-accentuated outputs.

**Usage**:
- Install the required libraries:
  pip install numpy opencv-python

  **Steps to run the script:**
    1) specify the input_folder that has the faceimages and the output_folder to get the composite sketches
    2) python cat.py
    
Adjust gamma in the adjust_gamma() function for brightness/contrast fine-tuning.


### **2. Deblurring Module**

Description: A GAN-based module implemented in TensorFlow, trained specifically to restore blurred facial sketches. The module leverages a deep convolutional generator and a patch-based discriminator.

**Key Features**:
-    **Generator**: Transforms blurred images into sharp images using a combination of convolutional, residual, and transposed convolutional layers.
-    **Discriminator**: Employs convolutional layers to distinguish real and synthetic sketches.
-    **Loss Function:** Optimized using Wasserstein loss for stability during training.

**Usage:**
- Install TensorFlow and required dependencies:
  pip install tensorflow

 **Steps to Train:**
 1. Prepare Input data: Place the folder of clear sketches in the root directory of this project. Rename it to clear_sketches
 2. Run the blur code to introduce artificial blur (You can choose any blurring technique ).
 3. python deblurring.py

 **Steps for Inference:**
 1. place your Blurred sketches in the folder names blurred_input (.jpg, .png )
 2. python deblur_inference.py
 3. Deblurred images are saved in a folder named deblurred_output.
 4. The filenames will be prefixed with deblurred_.

### **3. Image Translator Module**

Description: Converts sketches into photo-realistic images using a GAN architecture, preserving structure and enhancing realism.

## Features
- **Generator**: Converts input sketches to realistic images using residual blocks.
- **Discriminator**: Distinguishes between real and generated images using a patch-based structure.
- **Dataset**: Loads paired sketches and images from folders for training.
- **Training Pipeline**: Trains the generator and discriminator alternately to improve the quality of generated images.
- **Image Generation**: Saves generated images from input sketches in a specified output folder.

**Usage:**
Install the following Python libraries:
- `torch`
- `torchvision`
- `Pillow`

**Steps to execute**
1.  Place input sketches in the sketches folder and place corresponding real images in the images folder for training.
2.  python sketch_to_image.py
3.  After training, the script automatically processes the sketches in sketches/ and saves the generated images in the generated_images/ folder.

Thank you...