Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm
Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm

A maximum a posteriori (MAP) algorithm is proposed to improve the accuracy of super resolution (SR) reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction in the framework, but separates them in the process of iteratiion.Firstly, we estimate the shifting parameters through two low resolution (LR) images and use the parameters to reconstruct initial HR images. Then, we update the shifting parameters using HR images. The aforementioned steps are repeated until the ideal HR images are obtained.The metrics such as PSNR and SSIM are used to fully evaluate the quality of the reconstructed image. Experimental results indicate that the proposed method can enhance image resolution efficiently.
A maximum a posteriori (MAP) algorithm is proposed to improve the accuracy of super resolution (SR) reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction in the framework, but separates them in the process of iteratiion.Firstly, we estimate the shifting parameters through two low resolution (LR) images and use the parameters to reconstruct initial HR images. Then, we update the shifting parameters using HR images. The aforementioned steps are repeated until the ideal HR images are obtained.The metrics such as PSNR and SSIM are used to fully evaluate the quality of the reconstructed image. Experimental results indicate that the proposed method can enhance image resolution efficiently.