The invention discloses a super-resolution reconstruction method based on feature fusion of dual-channel convolution network, which comprises the following steps: a dual-channel convolution network isbuilt based on dense convolution network of different convolution cores; The dual channel convolution network comprises: two sub-channels, each sub-channel adopts the structure of dense connection network, the structure is generated by cascading a plurality of dense connection blocks, each dense connection block is composed of a 1* 1 convolution layer, a 3 *3 convolution layer and a hop layer connection, and a PRELU layer is used as a nonlinear activation function in front of each convolution layer; The weighted L1 norm is used as the loss function. After each sub-channel, the image is reconstructed with super-resolution, and the loss function is calculated and the parameters of the model are optimized. The loss function is the weighted sum of the calculated loss function of the sub-channel output image and the calculated loss function of the whole output; Input any size of low-resolution image, load the trained model, and output the reconstructed high-resolution image.