Underwater images usually have problems such as low contrast and color imbalance, which lead to unclear image texture information. Aiming at finding a solution, an underwater optical image sharpening method based on fusion of channel quantization and red prior is proposed. First, two input image versions are designed. Image 1 adjusts the image contrast by quantifying the color channel histogram and redistributes the pixel value. In image 2, in order to achieve color equalization, the red channel prior is substituted into the underwater imaging model to estimate the background light, direct component transmittance and backscattered transmittance. Then, three weight maps are designed for each input image, including brightness map, saturation map and saliency map. Finally, the multiscale fusion strategy is used to fuse the image after local contrast enhancement and color correction with its normalized weight map. The experimental evaluation carried out on multiple databases by subjective and objective indicators shows that the proposed algorithm can recover more color and detail information while presenting high contrast, effectively improve the quality of underwater images, and has advantages over other classical and novel algorithms.