Spatial resolution of images in phase scrambling Fourier transform imaging can be improved by post-processing signal band extrapolation. However, the improvements is small in the central area of image space. In our research, super-resolution using deep convolutional neural network are applied to temporally resolution improved images through iterative reconstruction. Simulation and experimental results showed that spatial resolution was fairly improved in the central area as well as in the edge of images. Proposed method is applicable to phase varied images by using adequately estimated phase distribution map since signal under-sampling is not utilized in proposed method.