To address the issue of picture blur and color distortion in underwater images of complex water bodies, an underwater image restoration algorithm based on HSV classification, CIELAB equalization, and minimum convolution region dark channel prior (DCP) is proposed. By the thresholds of H and S, the underwater photos are separated into high saturation distortion, low saturation distortion, and shallow water images. Then, the underwater image is recovered using CIELAB equilibrium and adaptive image enhancement, where the system parameters of the categorized underwater image are estimated by minimum convolutional area DCP. The experimental findings demonstrate that the suggested solution is superior to the comparison algorithms in image restoration effect, evaluation quality, and real-time performance indicators. The average peak signal-to-noise ratio and structural similarity values are increased by 26.88% and 17.3% on average, respectively, and the underwater image quality measurement value is increased by 4.3%.