Underwater Image Synthesis from RGB-D Images and its Application to Deep Underwater Image Restoration
Takumi Ueda, Koki Yamada, Yuichi Tanaka · 2019
This paper proposes a method to generate synthesized underwater images from clean RGB-D images taken on the ground. It is beneficial for training a deep neural network for underwater image restoration (UWIR), and also for measuring the performances among UWIR methods. The underwater images are synthesized on the modeling of an accurate degradation process with the consideration of absorption and scattering as well as ten water types. The water types result in different attenuation coefficients, i.e., different synthesized images. In the experimental results, it is validated that our method successfully synthesizes underwater images, and presents a state-of-the-art performance for UWIR by utilizing our synthesized images for the training of deep learning-based UWIR.