Single image super-resolution reconstruction method combining depth learning and gradient transformation

The invention discloses a single image super-resolution reconstruction method combining depth learning and gradient transformation. The method comprises the steps that a super-resolution method based on depth learning is used to carry out upsampling on an input low-resolution image to acquire an upsampling image; a gradient operator is used to carry out gradient-extracting on the upsampling image; a depth convolutional neural network is used to convert extracted gradient; a cost function is reconstructed by using the input low-resolution image and the converted gradient as constraints; a gradient descent method is used to optimize the reconstructed cost function to acquire a final output high-resolution image. According to the single image super-resolution reconstruction method provided by the invention, the reconstructed image has a fine structure in the subjective visual effect, is free of artificial effect, and has a high objective evaluation parameter value. The invention provides the effective single image super-resolution reconstruction method.