AI-ML based Intelligent de-hazing algorithm

Introduction

This project aims to develop a deep learning model to dehaze images. Image dehazing is pivotal for enhancing visual clarity in various applications in computer vision tasks. The model is constructed using an encoder-decoder architecture in PyTorch.

Dataset

We are utilizing the Dense-Haze dataset available on Kaggle. This dataset contains 55 pairs of hazy and non-hazy images, making it a suitable benchmark for image dehazing tasks.

Project Steps

1. Data Preprocessing:

   • Resize images and normalize pixel values.

2. Model Development:

   • Design an encoder-decoder neural network architecture.
   • Initialize model layers, including convolutional layers, pooling layers, and upsampling layers.
   • Define the forward propagation mechanism.

3. Training the Model:

   • Define loss function (Mean Squared Error) and optimizer (Adam).
   • Train the model using the training dataset.

4. Model Evaluation:

   • Compute evaluation metrics, including Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM).

5. Results Visualization:

   • Showcase original, hazy, and dehazed images side by side.
   • Plot training loss and validation loss across epochs.

6. Optimization (if necessary):

   • Tune hyperparameters, such as learning rate, batch size, and number of epochs.
   • Modify the model architecture by adding more layers or changing layer configurations.
   • Implement data augmentation to artificially increase the training dataset size and robustness.

7. Deployment:

   • Convert the trained model into a format suitable for deployment.
   • Integrate the model into a web or mobile application for real-time image dehazing.

Technical Details

• Framework: PyTorch
• Model Architecture: Encoder-Decoder CNN
• Loss Function: Mean Squared Error (MSE)
• Optimizer: Adam
• Evaluation Metrics: PSNR, SSIM

Future Work

1. Experiment with advanced architectures like U-Net or ResNet.
2. Implement a custom loss function for improved performance.
3. Enhance the deployment solution to process video streams for real-time dehazing.

Contributions

Contributions to this project are welcome. Please ensure that you adhere to the project's coding and documentation standards.

Acknowledgments

Special thanks to the creators of the Dense-Haze dataset for making it publicly available on Kaggle.