ProVision - Weather Effect Removal from Images

Enhancing Image Quality Using Image Processing & Deep Learning

Project Overview

Weather conditions such as rain, fog, haze, and snow degrade image quality, impacting various real-world applications like autonomous driving, surveillance systems, and satellite imaging. This project presents a hybrid approach that integrates traditional image processing and deep learning techniques to remove weather effects and restore image clarity.

Key Features

• Haze Removal: Implemented using Dark Channel Prior (DCP) method.
• Rain Streak & Drop Removal: Uses U-Net with Attention Mechanism & MPRNet for effective restoration.
• Snow Removal: CNN-based techniques for noise reduction.
• Performance Evaluation: Assessed using PSNR, SSIM, MSE Hybrid Loss, and contrast improvement metrics.
• Dataset Handling: Collected from Kaggle, GitHub repositories, and Google Images with preprocessing steps for improved results.

Tech Stack

• Programming Language: Python
• Deep Learning Frameworks: TensorFlow, Keras, PyTorch
• Image Processing: OpenCV, NumPy
• Models Used:
  • U-Net with Attention Mechanism
  • RIDNet (Deep Learning for Single-Image Deraining)
  • MPRNet (Multi-Stage Processing for Rain Removal)