Airlight estimation in underwater image restoration

Abstract

Underwater imaging is plagued by light absorption and scattering, resulting in distorted, blurry, and low-contrast. This paper introduces an innovative underwater image restoration algorithm that combines natural lighting-based airlight estimation with the refined dark channel prior. The algorithm directly estimates airlight, considering various underwater conditions such as depth, water quality, and camera-object distance, using the Jaffe-McGlamery underwater image formation model tailored for real-world underwater scenarios. A transmission map formula rooted in the refined dark channel prior is then derived. Finally, the algorithm employs the estimated airlight and transmission map to restore the image. Experimental results validate the algorithm's effectiveness in removing airlight artifacts, enhancing image contrast, and providing a clearer and more natural visual output. This approach promises to advance the quality of underwater imaging and its applicability across various domains.