Accurate underwater model based dataset and analysis

抄録

Although underwater images are important in many fields, image degradation such as color distortion or declined contrast caused by the complex ocean environment is a serious problem. In order to remove strong noises in underwater images, learning based approaches like deep learning are a prominent solution, but making large underwater dataset is a challenging task, not as in land images. Artificial images are commonly used in stead of real images to satisfy sufficient data in underwater image processing, but previous underwater image models are simplified and lacking reality. In order to enhance underwater images, this research constructs large underwater dataset based on correct underwater image model. Also, analysis of the constructed dataset and the performance of the proposed model is presented. PSNR of the proposed dataset distributed in wider range, suggesting the reality of the proposed dataset.