Underwater robotics is an important part of oceanography, resource exploration, and marine engineering. The images captured by underwater robots are affected by environmental factors, such as scattering and absorption, which lead to color cast and haze issues. We propose a light-weight fusion-based convolution neural network (FCNN) that improves the visual content of the underwater image. In FCNN, two methods, such as automatic white balancing and contrast limited adaptive histogram equalization, are used in the preprocessing phase. Then the outputs of the preprocessing phase are fed into the multilayer neural network to learn the end-to-end features. Finally, the outputs of the preprocessing phase and multilayer neural network are fused to obtain enhanced image. The performance of FCNN is evaluated based on qualitative, quantitative analysis, and in terms of time complexity. The experimental analysis proves that the FCNN overcomes the existing conventional methods and deep learning-based networks.