An Investigative Study on Deep Learning-Based Image Dehazing Techniques

DOI

10.2991/978-94-6463-471-6_9How to use a DOI?

Keywords

Dehazing; Deep Learning; CNN; GAN

Abstract

Image dehazing is a complex challenge within the field of computer vision, particularly when dealing with hazy or foggy scenes. Photographs captured in unfavourable weather conditions (such as haze, fog, smog, and mist) often suffer from significant degradation. These deteriorated images pose difficulties for various computer vision applications, including video surveillance, smart transportation, weather forecasting, and remote sensing. The task of mitigating these adverse effects is commonly referred to as image dehazing. In recent years, deep learning (DL) techniques have garnered substantial attention for addressing challenging image dehazing problems. Notably, architectures like Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) have revolutionized the field. CNNs excel at capturing spatial hierarchies and extracting meaningful features, while GANs leverage adversarial training to enhance the fidelity of dehazed images. By combining feature extraction and reconstruction, these DL models enable the restoration of clarity in hazy scenes. This article provides an extensive analysis of DL-based dehazing methods proposed by various researchers. It covers their performance, datasets used, evaluation metrics, and recent advancements. The goal is to improve the effectiveness and precision of dehazing algorithms, ultimately benefiting a wide range of applications.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - G. L. Narasamba Vanguri
AU  - Sangram Keshari Swain
AU  - M. Vamsi Krishna
PY  - 2024
DA  - 2024/07/30
TI  - An Investigative Study on Deep Learning-Based Image Dehazing Techniques
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 87
EP  - 97
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_9
DO  - 10.2991/978-94-6463-471-6_9
ID  - Vanguri2024
ER  -