IMAGE SUPER-RESOLUTION FUSION BASED ON HYPERACUTIY MECHANISM AND HALF QUADRATIC MARKOV RANDOM FIELD

In the image super-resolution reconstruction (SRR) process, the uncertainty factors such as the accuracy level of registration and the constraint method to solution will affect the reconstructed result. In this paper, we propose an SRR method using the combined hyperacuity mechanism with half quadratic Markov random field (MRF) in the frame of maximum a posteriori (MAP). A steepest-descent optimization algorithm is used to find the high resolution image. In the process of optimization, the initial estimate of high resolution image is firstly obtained by fusing the whole low resolution images inspired by the visual hyperacuity mechanism of flying insects. Then, the registration parameters and high resolution image are implemented jointly in order to reduce the uncertainty of image registration. Moreover, the adaptive regularization method is used to reduce the effect of randomness by man-made adjustment. The experimental results demonstrate our proposed method effective.