Underwater optical images are of significance to measure detailed underwater environments and have attracted much attention. However, underwater images are degraded by physical processes such as absorption or scattering to blur image characteristics which impedes diverse image analysis. In particular, multiple scattering caused by turbid water reduces the visibility of underwater images to prevent efficient operations such as in marine construction or resource exploration. It is still challenging to enhance the turbid underwater images by means of physical models or deep learning; due to multiple scattering often simplified in physical models or less available data to train deep models. In this study, we propose a novel underwater image enhancement (UIE) method tailored for highly turbid underwater images. Our UIE method is built upon classic image processing techniques, Local Laplacian Filter (LLF) and Discrete Wavelet Transform (DWT), to effectively augment blurred image characteristics. Experiments on two UIE benchmark datasets demonstrate that the proposed LLF-Fusion produces the competitive performance in comparison to the other UIE techniques. We also conduct physical experiments with a water tank to verify the efficacy of the proposed method for 3D geometry reconstruction in turbid water.