Case Study of A Calibration Problem in Acquired Hyperspectral Images

Underwater imaging severely suffers from color distortion and poor visibility due to light refraction and absorption and suspended particles in the water. Existing learning-based underwater image restoration methods highly rely on paired training datasets. However, distorted/non-distorted image pairs in realistic underwater scenes are unavailable and synthetic data-based trained models greatly degrade the performance in real-world applications. in this paper, an unpaired underwater image restoration method is proposed using a cycle adversarial generative network (CycleGAN) with an integrated distortion adaptive (DA) module. CycleGAN is used to perform image-to-image translation and the DA module can learn various degradation information from the unpaired training samples with flexible adaption. Experimental results demonstrate the superiority of our proposed method against existing state-of-the-art algorithms in restoring underwater images in terms of color correction and visual quality improvement.