In order to explore the application of deep learning theory in the problem of motion compensated frame interpolation, a DCNN (deep convolutional neural network) built with convolutional blocks and deconvolutional blocks was proposed. The proposed DCNN is capable of processing input images with different resolutions and preserving finegrained image details. The temporal coherent image sequences were used to construct the training sample and the stochastic gradient descent method was adopted to train the designed DCNN. Qualitative and quantitative experiments show that the trained DCNN obtains better interpolated images than the traditional approach in two testing images sequences.