Super-resolution image restoration algorithm based on Poisson-Markov model

The aim of image restoration is to achieve super-resolution and increase signal-noise ratio. Poisson-ML image restoration algorithm (PML) has high super-resolution performance, but introduces oscillatory artifacts in the restored image and can not get a ideal restoration result for the noisy image. Super-resolution image restoration algorithm based on Poisson and Markov model as well as the adaptive choice method of the regularization parameter (MPML) is proposed through the assumption of Poisson and Markov random field. Experiments demonstrate that MPML not only has high super-resolution performance, but also can effectively reduce and remove oscillatory artifacts in restored images, and get ideal restoration result for the noisy image. The significantly improved restoration results are obtained using MPML compared with PML. The regularization parameter can be automatically and optimally chosen in step with the restoration of the degraded image.