The Scanning Laser Ophthalmoscope (SLO) is an essential medical tool for diagnosis of retinal disease. It uses a small amount of laser to scan the retinal at high speed and transmits the fundus images to the video monitor for medical auxiliary diagnosis. However, like all optical imaging technologies, due to the interference of hardware equipment and external conditions, it is often not ideal imaging. In most clinical cases of laser ophthalmoscope, only low-resolution retinal images can be used to assist medical diagnosis. For this reason, we propose a new depth super-resolution method of retinal image based on laser scanning ophthalmoscope. The retinal image enhanced by local Laplacian operator is introduced into an efficient full convolution neural network. The convolution network uses Adam algorithm to replace the traditional SGD(Stochastic gradient descent) method, which runs faster and faster, and the reconstructed image effect is better. In this work, we subjectively evaluate our algorithm, apply it to real retinal images and compare it with several traditional super-resolution reconstruction methods. The experimental results show that this method has achieved good results in improving the overall quality of laser scanning ophthalmoscope image.