Image dehazing Using CNN

Project Code :TCMAPY1376

Objective

The primary objective of this project is to develop a robust image dehazing model utilizing a Global Memory Attention Network (GMAN) that effectively classifies hazy images as either clear or hazy. By employing a global attention mechanism, the model aims to enhance the focus on significant features within images, leading to improved classification accuracy and visual quality.

Abstract

This project presents a model integrated with a Global Memory Attention Network (GMAN) for image dehazing. The primary objective is to enhance the visual quality of hazy images by accurately classifying them as either hazy or clear. GMAN introduces a global attention mechanism, enabling the model to focus on significant features across the entire image, thereby improving classification accuracy. The dataset comprises various hazy and clear images, facilitating robust training and evaluation of the model. The proposed approach demonstrates significant potential in real-world applications, such as improving visibility in outdoor photography, enhancing computer vision tasks, and aiding automated surveillance systems. The expected output will enable users to determine the quality of input images, promoting advancements in image processing and analysis.

Keywords: Image dehazing, Global Memory Attention Network (GMAN), hazy image classification, feature extraction, computer vision.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.