A Cutting-Edge Framework for Efficient Image Dehazing and Accurate Image Segmentation Using Advanced Deep Learning Techniques

DOI

10.2991/978-94-6463-471-6_82How to use a DOI?

Keywords

Image dehazing; Image segmentation; Deep learning; Generative adversarial networks; Convolutional neural networks; Object extraction

Abstract

In recent years, image dehazing and image segmentation have emerged as vital tasks in computer vision, with numerous applications in various fields. This paper presents a cutting-edge framework that combines advanced deep-learning techniques to address the challenges associated with efficient image dehazing and accurate image segmentation. The proposed framework leverages convolutional neural networks (CNNs) and generative adversarial networks (GANs) to enhance the quality of hazy images and to accurately segment objects within the images. First, a specially designed CNN architecture is employed to learn effective features from hazy images, enabling the model to estimate and remove the haze efficiently. Next, a GAN- based approach is integrated into the framework to refine the dehazed images and alleviate artifacts commonly introduced during the dehazing process. Furthermore, an improved segmentation network is utilized to accurately identify and extract objects of interest from the dehazed images, offering precise and reliable segmentation results. Overall, this work contributes to the advancement of image processing techniques and offers a valuable solution for enhancing the quality of hazy images and performing accurate object segmentation in various 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.

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TY  - CONF
AU  - Mithinesh Jaya Kumar Sankarapu
AU  - D. Shanmugaraj
AU  - G. Kalairasi
AU  - M. Selvi
AU  - G. Yogitha
AU  - E. Srividhya
PY  - 2024
DA  - 2024/07/30
TI  - A Cutting-Edge Framework for Efficient Image Dehazing and Accurate Image Segmentation Using Advanced Deep Learning Techniques
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 866
EP  - 875
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_82
DO  - 10.2991/978-94-6463-471-6_82
ID  - Sankarapu2024
ER  -