Image Dehazing Flask App in Python Projects

Image Dehazing Flask App in Python Projects
Abstract
Image dehazing is an important image enhancement task in computer vision, aiming to restore the clarity and visibility of images affected by haze, fog, or smoke. This project focuses on developing a Python-based Image Dehazing system deployed through a Flask web application. The system uses deep learning models, particularly Convolutional Neural Networks (CNN), to remove haze from images automatically while preserving important details and colors. Implemented with Python libraries such as TensorFlow/Keras, OpenCV, NumPy, and Flask, the application allows users to upload hazy images via a web interface and receive dehazed, high-quality outputs in real time. The system is useful for outdoor photography, surveillance, autonomous vehicles, and remote sensing.
Existing System
Traditional image dehazing techniques rely on physical models like the Dark Channel Prior, contrast enhancement, or histogram-based approaches. While these methods can partially remove haze, they often struggle with color distortion, loss of details, or require manual parameter tuning. Existing automated solutions may not generalize well to diverse environmental conditions or real-time applications and usually lack interactive web-based deployment for user-friendly usage.

Proposed System
The proposed system integrates deep learning-based dehazing with a Flask web interface for real-time usability. Hazy images are preprocessed using resizing, normalization, and data augmentation to improve model accuracy. A CNN or U-Net architecture is trained on paired datasets of hazy and clear images to learn mapping from hazy input to clear output. The Flask application allows users to upload images, initiate dehazing, and visualize results instantly. Python libraries such as OpenCV handle image preprocessing, TensorFlow/Keras implement the dehazing model, NumPy manages data arrays, and Flask provides the interactive web deployment. By combining deep learning and web-based accessibility, the system offers an automated, accurate, and user-friendly solution for restoring image clarity in diverse real-world conditions.

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