Underwater Image Restoration Based on Image Blurriness and Light Absorption

TLDR

Underwater images suffer color distortion and low contrast from light scattering and absorption, and varying lighting conditions make restoration difficult. The study proposes a depth estimation method for underwater scenes based on image blurriness and light absorption to improve image‑formation‑model‑based restoration. The method derives scene depth by analyzing image blurriness and light absorption and incorporates this depth into the image‑formation model for restoration and enhancement. Experiments on real and synthetic images show the proposed method yields more accurate depth estimates and outperforms existing IFM‑based underwater image restoration techniques.

Abstract

Underwater images often suffer from color distortion and low contrast, because light is scattered and absorbed when traveling through water. Such images with different color tones can be shot in various lighting conditions, making restoration and enhancement difficult. We propose a depth estimation method for underwater scenes based on image blurriness and light absorption, which can be used in the image formation model (IFM) to restore and enhance underwater images. Previous IFM-based image restoration methods estimate scene depth based on the dark channel prior or the maximum intensity prior. These are frequently invalidated by the lighting conditions in underwater images, leading to poor restoration results. The proposed method estimates underwater scene depth more accurately. Experimental results on restoring real and synthesized underwater images demonstrate that the proposed method outperforms other IFM-based underwater image restoration methods.