The invention discloses an image super-resolution method based on a deep threshold convolutional neural network, which is mainly used to solve the problem that the super-resolution effect of an image is reduced when the network is deepened in the prior art. The method comprises the following steps: (1) acquiring paired low-resolution and high-resolution image data as training data; (2), defining a threshold convolution layer to replace the existing convolution layer, and building an end-to-end deep threshold convolutional neural network; (3) inputting the training data to the deep threshold convolutional neural network to train the network through an Adam optimization method; and (4) using the trained deep threshold convolutional neural network to carry out image super-resolution. The gradient attenuation problem of the deep neural network is reduced. The application of the deep network in image super resolution is realized. The effect of image super-resolution is enhanced. The speed of image super-resolution is increased. The image super-resolution method can be used in fields such as satellite remote sensing, medicine, traffic monitoring and video compression.