A super resolution reconstruction architecture utilizing registration error minimizing

The high-resolution image reconstruction methods utilizing a super-resolution (SR) technique better can obtain higher quality image than the conventional interpolation methods when the input images are well registered onto a common high-resolution grid. Therefore, low-resolution input images should be carefully selected to take the minimized registration error. In this paper, we propose the input image evaluation algorithm to select the suitable input image with low registration error, by using the statistical feature of the motion-compensated low-resolution images. Maximum motion compensation error (MMCE) is estimated from the high-resolution image observation model and is used to evaluate the suitability of the low-resolution input image candidates. The low-resolution input image is selected when its motion compensation error (MCE) is in the range 0 < MCE < MMCE. The reference input image (RII) is selected by counting the number of selected low-resolution input images (SLRII) from all low-resolution input images (LRII). A good high-resolution image is reconstructed efficiently from a optimal reference input image (ORII) and the selected low-resolution input images (SLRII) by using the Hardie's SR reconstruction algorithm.