Several new super-resolution image restoration algorithms based on orthogonal discrete wavelet transform are proposed, by using orthogonal discrete wavelet transform and generalized cross validation ,and combining with Luck-Richardson super-resolution image restoration algorithm (LR) and Luck-Richardson algorithm based on Poisson-Markov model (MPML).
Orthogonal discrete wavelet transform analyzed in both space and frequency domain has the capability of indicating local features of a signal, and concentrating the signal power to a few coefficients in wavelet transform domain. After an original image is "Symlets" orthogonal discrete wavelet transformed, an asymptotically optimal threshold is determined by minimizing generalized cross validation, and high frequency subbands in each decomposition level are denoised with soft threshold processes to converge respectively to those with maximum signal-noise-ratio, when the method is incorporated with existed super-resolution image algorithms, details of original image, especially of those with low signal-noise-ratio, could be well recovered.
Single operation wavelet LR algorithm(SWLR),single operation wavelet MPML algorithm(SW-MPML) and MPML algorithm based on single operation and wavelet transform (MPML- SW) are some operative algorithms proposed based on the method. According to the processing results to simulating and practical images , because of the only one operation, under the guarantee of rapid and effective restoration processing, in comparison with LR and MPML, all the proposed algorithms could retain image details better, and be more suitable to low signal-noise-ratio images, They could also reduce operation time for up to hundreds times of iteratives, as well as, avoid the iterative operation of self-adaptive parameters in MPML, improve operating speed and precision. They are practical and instantaneous to some extent in the field of low signal-noise-ratio image restoration.