Enhancing Underwater Images Using Deep Learning with Subjective Image Quality Integration
This work addresses image quality issues for underwater imaging applications, representing an incremental advancement by combining existing methods like classifiers and GANs with subjective criteria.
The paper tackles the problem of enhancing underwater images by integrating human subjective assessments into a deep learning training process, resulting in substantial improvements in both perceived and measured image quality, with specific metrics like PSNR, SSIM, and UIQM used for evaluation.
Recent advances in deep learning, particularly neural networks, have significantly impacted a wide range of fields, including the automatic enhancement of underwater images. This paper presents a deep learning-based approach to improving underwater image quality by integrating human subjective assessments into the training process. To this end, we utilize publicly available datasets containing underwater images labeled by experts as either high or low quality. Our method involves first training a classifier network to distinguish between high- and low-quality images. Subsequently, generative adversarial networks (GANs) are trained using various enhancement criteria to refine the low-quality images. The performance of the GAN models is evaluated using quantitative metrics such as PSNR, SSIM, and UIQM, as well as through qualitative analysis. Results demonstrate that the proposed model -- particularly when incorporating criteria such as color fidelity and image sharpness -- achieves substantial improvements in both perceived and measured image quality.