Blind Multi-image Super-resolution Reconstruction with Motion Blur Estimation

Blind image super-resolution reconstruction is a hot and difficult problem in image processing. A framework of blind multi-image super-resolution reconstruction is proposed. In the low-resolution imaging model, the processes of movement and motion blur are considered. The horizontal shift and vertical shift between the low resolution images are estimated with sub-pixel precision. The parameter of motion blur is estimated through an error-parameter analysis method. Using Wiener filtering image restoration algorithm, an error-parameter curve at different motion distance is generated. By setting threshold, the motion distance of the motion blur can be estimated automatically. The super-resolution image is reconstructed through the Iterative Back Projection (IBP) algorithm. The experimental results show that the motion blur is estimated with high accuracy and that motion blur estimation plays an important part in improving the quality of the SR reconstructed image.