Image upscaling to obtain high quality digital image is one of the active research topics as it is applicable in the consumer electronics industries. Traditional image upscaling techniques have low computational complexity and applicable for real-time processing, but reconstructed image often contains artifacts and undesirable visual effect. The relationship between image interpolation and super-resolution leads our assumption that the interpolated image can be further optimized and may be considered as a part of super-resolution algorithm. In this paper, we propose a new image super-resolution method to combine fast image interpolation with iterative back-projection. This method does not require any external pre-trained datasets and has low computation time while the quality of the reconstructed image can be measured up to the high programming complexity methods such as the dictionary and deep convolutional neural networks.