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