TEBCF: Real-World Underwater Image Texture Enhancement Model Based on Blurriness and Color Fusion
Jieyu Yuan, Zhanchuan Cai, Wei Cao
Abstract
Real-world underwater images suffer from quality degeneration caused by the scattering and absorption of light propagation. The damage of the detailed textures in underwater images shows the negative effect of detection and recognition. To recovery the image visibility and sharpness for the above applications, a new image enhancement method is proposed for extracting the image textures. To enhance the image textures with high quality, we propose a multiscale fusion enhancement. Two new fusion inputs are built on different color methods. One input is devoted to improve the sharpness by contrast-based dark channel prior dehazing in the red–green–blue (RGB) model. The other input is designed based on multiple morphological operation and color compensation from the opponent color in the CIE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1976~L^{\ast}a^{\ast}b^{\ast}$ </tex-math></inline-formula> color space (CIELAB) model. This input is used to enhance the counter brightness and adjust the color distribution. The dominant features of the two inputs are merged. Therefore, the contrast of the fusion output is enhanced adaptively to recover the final enhanced result. Compared with the state-of-the-art methods, our results reveal that the proposed method can enrich the image textures based on an impressive visual perception of contrast, saturation, and sharpness. Moreover, our method also shows strong robustness in challenge scenes and improves the performance of several underwater applications.