Underwater Image Enhancement Based on Adaptive Correction and Fusion
Qingfeng Ta, Haijun Xie, Binghua Su, Zefeng Zhao, Zhuang Zhou · 2022
Underwater applications require clear underwater images, however, blurring and contrast degradation in underwater images are caused by absorption, dispersion, and water mass. To address the limitations of existing color correction and deblurring algorithms, this study proposes a fusion-based image enhancement algorithm. Two images obtained by adaptive channel defogging technique and restricted histogram mapping interval correction technique are used as two inputs to the fusion framework to obtain a new underwater image. We tested our technique with a public dataset. The experimental evidence shows that the technique proposed in this paper can improve the quality and detail of blurred underwater images. This paper also demonstrates that the method has superior deblurring and color correction capabilities than other existing algorithms.