Reconstruction of super resolution for noise image under the sparse representation

In order to complete the super-resolution reconstruction of noise images, a reconstruction method of noise images was introduced based on sparse representation, which could complete image de-noising and super resolution reconstruction simultaneously. Firstly, block size division was made for sample images and low-resolution images and the sample database was established. Secondly, the image degradation model was built and the way of weighted average was used for similar samples to represent the output image block with high resolution. Then, according to the input low-resolution image block, the similarity between sample block and output high-resolution image block was calculated. In addition, a similarity description method which could better reduce the influence bought by noises was proposed. Using the similarity to punish the sparse coding optimization models, a weight solving model was established. And the similar sample model could be self-adaptively searched by the model rather than being set the number of similar blocks in advance. Finally, the image block with high resolution as well as high-resolution images were reconstructed, according to the solved weight and input sample block. The result of experiment shows: compared with the other common super resolution algorithms, the peak signal to noise ratio of the mentioned method improves approximately 0.5 dB; and the reconstructed image with more details has better de-noise effect and is more suitable to practical use.