Due to the refraction, absorption, and scattering of light by suspended particles in water, underwater images have low contrast, blurred details, and colour distortion. The Very-Deep Super-Resolution (VDSR) reconstruction model is introduced to increase the resolution of images captured using underwater applications. A residual learning model for underwater image enhancement has been introduced in this paper. The CNN layers are estimated and applied to the images in order to obtain the feature map. Based on this feature map, the particles are removed. According to the underwater image enhancement experiments and a comparative analysis, the colour correction and detail enhancement performance of the proposed methods are superior to that of previous deep learning models and traditional methods. The experimental results suggest that this method produce better results when compared to state-of-art methods.