New Approach to Underwater Image Enhancement Using Modified Residual Blocks in Generator Architecture for Improved Cycle Generative Adversarial Networks

Abstract

Images captured underwater frequently have a low resolution as a result of a number of issues including light attenuation, backscattering, and colour distortion. The restoration of underwater images, which serves as an essential building block for the field of underwater vision research, remains a difficult endeavor. The process of removing the haziness and the colour distortion caused by the underwater environs is the main focus of the work that goes into the restoration of underwater images. Within the confines of this research, we present an enhanced approach for the enhancement of underwater images called Improved Cycle GAN (Generative Adversarial Network). The suggested approach makes use of a dual architecture that is composed of a generator network and a discriminator network in order to learn the mapping between low-quality underwater photographs and high-quality images. This dual architecture is comprised of a generator network and a discriminator network. The generator network is trained to transform the input image into an enhanced image, while the discriminator network evaluates the realism of the generated images. The suggested method outperforms state-of-the-art visual quality methods on a real-world UFO underwater image dataset. The proposed method is used to recover the original image. In order to measure quantity, the underwater image quality measure attributes called underwater image colourfulness measure (UICM), underwater image sharpness measure (UISM), and underwater image contrast measure (UIConM) are assessed. The proposed method could be employed in various underwater imaging processing applications, such as underwater surveillance, marine biology research, and underwater exploration, where high-quality images are crucial for effective analysis and decision-making.