Design and analysis of high-performance real-time image dehazing using convolutional neural and generative adversarial networks

Partha Kaushik; Avi Gupta; Hemkant Nehete; Raman Balasubramanian; Brajesh Kumar Kaushik

doi:10.1117/12.2651023

15 March 2023 Design and analysis of high-performance real-time image dehazing using convolutional neural and generative adversarial networks

Partha Kaushik, Avi Gupta, Hemkant Nehete, Raman Balasubramanian, Brajesh Kumar Kaushik

Author Affiliations +

Proceedings Volume 12438, AI and Optical Data Sciences IV; 124380M (2023) https://doi.org/10.1117/12.2651023
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Optical imaging sensors suffer from distortions caused by atmospheric particles such as dust, mist, fog, haze, and smoke, resulting in degradation of object detection and recognition. To circumvent these issues, image dehazing is an essential preprocessing stage for various real time applications. Several conventional dehazing methods rely on the haze formation model that are inherently dependent on a large number of variables, requiring huge computational burden on the processor. This severely affects the dehazing performance and also restricts real time processing. To overcome these issues, this work deals with an end-to-end real time dehazing architecture based on light weight Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). Proposed depthwise seperable and residual (DSR) block has been used instead of convolution layers that significantly lowered the parameters and computations. Furthermore, sigmoid and bilateral ReLu activation functions have been exploited to prevent oversaturation of dehazed images. The proposed model achieves significant enhancements in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for both synthetic and real-world hazy images, when compared to other architectures such as dark channel prior (DCP) and DehazeNet. The performance outcome of CNN and GAN based dehazing architectures are analyzed and compared.

Conference Presentation

Citation Download Citation

Partha Kaushik, Avi Gupta, Hemkant Nehete, Raman Balasubramanian, and Brajesh Kumar Kaushik "Design and analysis of high-performance real-time image dehazing using convolutional neural and generative adversarial networks", Proc. SPIE 12438, AI and Optical Data Sciences IV, 124380M (15 March 2023); https://doi.org/10.1117/12.2651023

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available