A Cutting-Edge Framework for Efficient Image Dehazing and Accurate Image Segmentation Using Advanced Deep Learning Techniques
Mithinesh Jaya Kumar Sankarapu, D. Shanmugaraj, G. Kalairasi, Marco Selvi, G. Yogitha, E. Srividhya · Advances in computer science research · 2024
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 deeplearning 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.