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Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm 被引量:1

Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm
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摘要 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 lowresolution( 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 lowresolution( 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.
出处 《Journal of Beijing Institute of Technology》 EI CAS 2018年第2期237-240,共4页 北京理工大学学报(英文版)
基金 Supported by the National Natural Science Foundation of China(61405191)
关键词 super-resolution(SR) maximum a posteriori(MAP) peak signal to noise ratio structure similarity super-resolution(SR) maximum a posteriori(MAP) peak signal to noise ratio structure similarity
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