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In this article, we propose a super-resolution method to resolve the problemof image low spatial because of the limitation of imaging devices. We make useof the strong non-linearity mapped ability of the back-propagation neuralnetworks(BPNN). Training sample images are got by undersampled method. Theelements chose as the inputs of the BPNN are pixels referred to Non-localmeans(NL-Means). Making use of the self-similarity of the images, those inputsare the pixels which are pixels gained from modified NL-means which is specificfor super-resolution. Besides, small change on core function of NL-means hasbeen applied in the method we use in this article so that we can have a cleareredge in the shrunk image. Experimental results gained from the Peak Signal toNoise Ratio(PSNR) and the Equivalent Number of Look(ENL), indicate that addingthe similar pixels as inputs will increase the results than not taking theminto consideration.

Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network