Abstract
Super-resolution image restoration technique can certainly increase the resolution of the obtained images without altering the system hardware conditions, which has become a research hotspot in the fields of remote sensing, military surveillance, public safety and medical imaging. The spatial domain algorithm has been studied widely and deeply, but it still has shortcomings and limitations. This paper studies the image degradation mechanism, establishes a precise image degradation process model, proposes an improved hybrid MAP-POCS restoration algorithm with using the Huber-Markov random field model as the priori probability model, which adds the convex sets constraints to the MAP estimation process and uses the peak signal to noise ratio (PSNR) to evaluate the recovery image quality. The simulation results show that the improved hybrid algorithm combines the only solution solving feature and noise reduction ability of the MAP method and strong prior knowledge utilized feature and flexibility of the POCS method, effectively ensures the convergence stability of the restoration and maintains the edge details of the restored image, enhances the effect of the super-resolution restoration.
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