Enhancing underwater image via adaptive color and contrast enhancement, and denoising

Images captured under water are often characterized by low contrast, color distortion, and noise, hindering some visual tasks carried out on it. Despite remarkable breakthrough has been made in recent years, effective and robust enhancement of degraded image remains a challenging problem. To improve the quality of underwater images, we propose a novel scheme by constructing an adaptive color and contrast enhancement, and denoising (ACCE-D) framework. In the proposed framework, Difference of Gaussian (DoG) filter and bilateral filter are respectively employed to decompose the high-frequency and low-frequency components. Benefited from this separation, we utilize soft-thresholding operation to suppress the noise in the high-frequency component. Specially, the low-frequency component is enhanced by using an adaptive color and contrast enhancement (ACCE) strategy. Moreover, we derive a numerical solution for ACCE, and adopt a pyramid-based strategy to accelerate the solving procedure. Both qualitative and quantitative experiments demonstrate that our strategy is effective in color correction, contrast enhancement, and detail revealing. In the quantitative evaluations, by performing on the 890 real-world underwater images from UIEBD, the proposed method obtains 0.65 UCIQE, 1.59 UIQM, 0.81 FDUM, 1.34 PCQI, 0.62 CBPD, and 7.75 entropy scores, achieving average increase of 5% comparing with several state-of-the-art methods. Furthermore, we have verified the utility of our proposed ACCE-D for enhancing other types of degraded scenes, including foggy scene, sandstorm scene and low-light scene.

Automated summary

The study proposes a novel scheme for enhancing underwater images, utilizing a Difference of Gaussian (DoG) filter and bilateral filter to decompose the high-frequency and low-frequency components to suppress noise and enhance color and contrast adaptation as well.