Underwater image enhancement via complementary advantage fusion of global and local contrast

Abstract

Underwater images encounter a range of quality degradation challenges due to most wavelengths of light being attenuated by varying degrees of absorption in traveling underwater. To cope with these issues, we present a complementary advantage fusion method of global and local contrast, named CAFM. Successively, CAFM first compensate for the attenuation of each channel by exploiting the pixel intensity and distribution of each channel to get an image without color distortion. Subsequently, we employ the double histogram optimization contrast method to enhance the global contrast of the preprocessed image, while the mean and variance features of image blocks are utilized to enhance the local contrast of the preprocessed image. To get a high-quality underwater image, we employ a complementary advantage fusion method to combine the benefits of the two enhanced images via the complementary advantages between different feature maps. Extensive evaluation of three datasets demonstrates that our CAFM surpasses the compared methods. Additionally, the underwater images are enhanced by our CAFM with authentic colors, heightened contrast, and rich texture detailing.