Underwater Image Enhancement Using Scene Depth-Based Adaptive Background Light Estimation and Dark Channel Prior Algorithms

Abstract

Due to the complexity of the underwater environment, underwater images captured by optical cameras usually suffer from haze and color distortion. Based on the similarity between the underwater imaging model and the atmosphere model, the dehazing algorithm is widely adopted for underwater image enhancement. As a key factor of the dehazing model, background light directly affects the quality of image enhancement. This paper proposes a novel background light estimation method which can enhance the underwater image. And it can be applied in 30-60m depth with artificial light. The method combines deep learning to obtain red channel information of the background light in the dark channel of the underwater image. Then, the background light is obtained by adaptive color deviation correction. Finally, the experiments of underwater images enhancement are carried out, using the dark channel prior algorithm based on the proposed background light estimation method. The results show that the proposed method effectively improves underwater image blur and color deviation, and is superior to other methods in multiple non-reference image evaluation indicators.

Keywords

• Adaptive background light estimation
• color correction
• deep learning
• dark channel prior
• underwater image enhancement