Development of Attentive-Based Trans-UNet and Cycle Generative Adversarial Networks for Underwater Image Enhancement

Researchers across the globe have been investigating underwater photographs and a way to capture pictures with outstanding clarity for the past couple of decades. Also, improving the obtained photographs is an exhausting task. Usually, applied underwater image-capturing devices are unable to acquire high-resolution photos underwater, and their maintenance is extremely costly. The underwater images include multiple flaws because of biological processes like attenuation and scattering. These photographs experience color distortion, blurriness, and low contrast effects. Technologies that utilize deep learning have become more popular among several research studies and have gradually grown in impact on society. Several techniques demand sets of training photos, but gathering such expected sets can be challenging because of the complex nature of the underwater environment. Generating and restoring an image from water is a difficult task that has gained prominence in recent days. By lowering graininess, adjusting, and refining the photos using deep learning models, the major goal is to enhance underwater images. To accomplish this objective, an intelligent attention-based deep learning model is proposed. In the first stage, the unrefined images are gathered from typical data sources. Further, the collected underwater images are fed into the model of Attentive-based Trans-UNet-CycleGAN (ATUNet-CGAN), where the Transformer-based UNet model is integrated with the Cycle Generative Adversarial Networks (GANs). Also, the attention mechanism process is involved in Trans-UNet-CycleGAN for improving the superiority of submarine images. Finally, the performance of the model is validated using different metrics and correlated among baseline approaches. Therefore, the proposed methodology outperforms the exploitation of better enhancement of image quality.