Underwater photoelectric images have a low signal-to-noise ratio and poor contrast because water absorbs and scatters light. This makes it difficult to identify targets and limits the practical applications and the development of underwater optoelectronic imaging equipment. To improve the detection accuracy and recognition rate of the target, we propose a deep convolutional neural network with one-dimensional parallel convolution and sub-pixel convolution. The convolutional neural network is used to learn the parameters that can improve the image quality from the underwater photoelectric image training set. Then, it can denoise and enhance the contrast for the test images. The peak signal-to-noise ratio obtained using our method showed an average improvement of 2.93 dB over the ratio obtained using the classic denoising and contrast enhancement methods; the root mean square contrast also increased by an average of 14.41. Therefore, our proposed method can effectively balance the denoising, contrast enhancement, and brightness enhancement. This will improve the image quality. The average processing speed of a single image is 9.46 times greater than that of the classic method. Finally, the network is tested using the test set. And our network could improve the image quality and provide a generalization characteristic within a certain range.