  In this article, we propose a super-resolution method to resolve the problem
of image low spatial because of the limitation of imaging devices. We make use
of the strong non-linearity mapped ability of the back-propagation neural
networks(BPNN). Training sample images are got by undersampled method. The
elements chose as the inputs of the BPNN are pixels referred to Non-local
means(NL-Means). Making use of the self-similarity of the images, those inputs
are the pixels which are pixels gained from modified NL-means which is specific
for super-resolution. Besides, small change on core function of NL-means has
been applied in the method we use in this article so that we can have a clearer
edge in the shrunk image. Experimental results gained from the Peak Signal to
Noise Ratio(PSNR) and the Equivalent Number of Look(ENL), indicate that adding
the similar pixels as inputs will increase the results than not taking them
into consideration.
