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In order to improve the spatial resolution of the super resolution reconstructed image, an improved image super resolution reconstruction algorithm is proposed. In iterative back projection (IBP) algorithm, the initial value is estimated through wavelet locally adaptive interpolation method, other than in the traditional interpolation way. Image super resolution reconstruction is performed on multiple simulated low resolution images, experimental results show that the initial value plays an important part in super resolution reconstruction algorithm. The reconstructed image achieves better visual effect and higher peak signal to noise ratio (PSNR). The IBP algorithm has less estimation error and faster convergent speed than other method. This initial value estimation method can also be extensively used in other iterative super resolution algorithms.

To improve the spatial resolution of low resolution image with Gaussian blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering method. Then, the Gaussian blur of the de-noised image is estimated through error-parameter analysis method. Finally, super resolution reconstruction is carried out through iterative back projection algorithm. Experimental results show that the Gaussian blur is estimated with high accuracy, and the Pepper & Salt noise are removed effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved. In addition, the importance of Gaussian blur in single-image super resolution reconstruction is justified in an experimental way.

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