Supervised and Unsupervised Deep Learning Methods for Underwater Image Enhancement




Rico Espinosa, Alejandro

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Large amounts of underwater imagery are constantly collected for environmental monitoring studies, as they are essential for estimating marine biodiversity and abundance. However, this collected data has variable quality due to uncontrolled environmental factors that cause blur and color casting. We attempt to address this issue by proposing two novel methods for underwater image enhancement. The first part of the thesis presents a deep learning architecture that integrates elements from classical methods to simultaneously address blurriness and color casting on underwater imagery in real time. We use two parallel architectures trained in a generative adversarial network scheme (GAN) with channel and spatial attention blocks to retrieve color, and discrete wavelength transform to preserve high-frequency components. Our experiments show that our method outperforms the state-of-the-art related works with respect to the structured similarity index metric (SSIM). Qualitative comparisons with color-checkers also show notable improvements over related works. The second part of the thesis proposes an unsupervised deep-learning approach for underwater image enhancement, which eliminates the need for reference images for training. This is an important step forward as for real (not synthetic) underwater images there is no high-quality reference available. Our method is based on a mathematical model for image dehazing. We use three networks to estimate the transmission map, the atmospheric light, and the enhanced image and propose a new compound loss function. We achieve results comparable to state-of-the-art supervised methods with respect to the SSIM while performing optimally at near real-time inference speeds.



Unsupervised, Deep Learning, Supervised, Underwater, Image Enhancement, Color Correction