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In this paper two transform based super resolution methods are presented for enhancing the resolution of a stationary image. In the first method, neural network is trained by wavelet transform coefficients of lower resolution of a given image, and then this neural network are used to estimate wavelet details subbands of that given image. In this way, by using these estimated subbands as wavelet details and the given image as the approximation image, a super-resolution image is made using the inverse wavelet transform. In the second proposed method, the wavelet transform is replaced by contourlet transform and the same mentioned procedure is applied. These two methods have been compared with each other and with the bicubic method on different types of images. The experimental results demonstrate the superiority performance of the proposed methods compared with regular stationary image resolution enhancing methods.