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

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.

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

“…CNN architecture for deep learning, as opposed to end-toend mapping. 66 All-in-one dehazing (AOD) net employs linear mapping to integrate transmission maps (TðxÞ) and atmospheric light (A) and uses CNN to learn its parameter. 67 The mathematical equation is formulated on the AOD-net 67 methods as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 1 6 ; 1 1 7 ; 3 2 1…”

“…Generative adversarial nets (GAN) have several uses, including text-to-picture and image-to-image translation. 66,95,96 It has been suggested to use a U-net architecture 97 for the generator, which directly maps input to output image and aids in restoring signal independence from noise. WaterGAN is a technique that produces an accurate depth map from an underwater image.…”