The very deep super resolution model has disadvantages: the convergence speed is low, the original image must be preprocessed before training, and the network redundancy must be reduced. This study proposes a single-image super resolution reconstruction method based on depth jumping cascade (DCSR). First, DCSR eliminates pre-processing, extracts the shallow features directly on the low-resolution image, and finally uses sub-pixel convolution to magnify the image. Second, each convolutional layer is fully utilized to extract the image features using the jump cascading block, thereby realizing feature reuse and network redundancy reduction. The jump cascading block of the network establishes a short connection directly from the output to each layer, speeding up the network convergence speed and alleviating the gradient disappearance problem. The experimental results show that on several public datasets, the peak-signal-to-noise ratio and the structural similarity of the algorithm are higher than those of existing algorithms, which fully demonstrates an excellent algorithm performance.