De-scattering and edge enhancing are critical procedures for underwater images which suffer from serious contrast attenuation, color deviation, and edge blurring. In this paper, a novel method is proposed to enhance underwater images. Firstly, a Convolutional Neural Network (CNN) is trained end-to-end to estimate the transmission map. Simultaneously, the adaptive bilateral filter is used to refine the transmission map. Secondly, a strategy based on the white balance is proposed to remove the color deviation. Laplace pyramid fusion is utilized to obtain the fusion result of the haze-free and color-corrected image. Finally, the output image is transformed into the Hybrid Wavelets and Directional Filter Banks (HWD) domain for de-noising and edge enhancing. The experimental results show that the proposed method can remove color distortion and improve the clarity of the underwater images. Objective and subjective results demonstrate that the proposed method outperforms several state-ofthe-art methods in different circumstances.
Pan, Pan-wang; Yuan, Fei; and Cheng, En
"UNDERWATER IMAGE DE-SCATTERING AND ENHANCING USING DEHAZENET AND HWD,"
Journal of Marine Science and Technology: Vol. 26:
4, Article 6.
Available at: https://jmstt.ntou.edu.tw/journal/vol26/iss4/6