In this paper, we introduce a novel feature descriptor based on deep learning that trains a model to match the patches of images on scenes captured under different viewpoints and lighting conditions for Multi-frame super-resolution. The patch matching of images capturing the same scene in varied circumstances and diverse manners is challenging. We develop a model which maps the raw image patch to a low dimensional feature vector. As our experiments show, the proposed approach is much better than state-of-the-art descriptors and can be considered as a direct replacement of SURF. The results confirm that these techniques further improve the performance of the proposed descriptor. Then we propose an improved Random Sample Consensus algorithm for removing false matching points. Finally, we show that our neural network based image descriptor for image patch matching outperforms state-of-the-art methods on a number of benchmark datasets and can be used for image registration with high quality in multi-frame super-resolution reconstruction.