An Underwater Image Vision Enhancement Algorithm Based on Contour Bougie Morphology

TLDR

Underwater images suffer from scattering and light absorption, necessitating further enhancement to improve image quality. The study proposes a new Contour Bougie morphology‑based enhancement method to improve underwater image visibility and conducts a comprehensive comparison with state‑of‑the‑art algorithms to assess its effectiveness. The method employs two differently sized structuring elements as roving windows, applies multiple morphological operations to highlight details, normalizes and stretches the enhanced images for RGB white balance, and is evaluated on 890 raw underwater degraded images. Quantitative and qualitative evaluations on 890 images demonstrate that the method yields superior visible contrast and color balance, effectively highlighting undersea creature details.

Abstract

Underwater images require further enhancement to improve the image qualities caused by medium scattering and light absorption. Based on Contour Bougie (CB) morphology, we propose a new enhancement method to enhance the scene contours and improve the visibility of images captured underwater. Two structuring elements with different sizes are considered as the roving windows. Multiple morphological operations are designed for highlighting the rich details on the origin images. The enhanced images are normalized and stretched to improve the white balance of RGB channels. The comprehensive study of state-of-the-art algorithms is conducted to interpret the improvement of image quality by the proposed method. In addition, we use 890 raw underwater degraded images as the testing data. The quantitative and qualitative evaluations of these data demonstrate that the proposed method achieves better visible contrast for highlighting the details of the undersea creatures. The comparison with different underwater scenes proves that the proposed method improves the color balances of the degraded images.