Image super-resolution restoration and enhancement (SR) based on reconstruction is a typically ill-posed and high-dimensional problem, which needs effective regularization to stable the solution.
基于重建的超分辨率（SR）方法中，图像求解是典型的高维病态问题，需借助有效的正则来稳定求解。
Super resolution (SR) image reconstruction is a technique to recover a high resolution image from several low resolution images using the non-redundant information among them.
影像超分辨率重建是通过对多幅具有互补信息的低分辨率影像的处理，重构一幅高分辨率影像的技术。
In this paper, a new joint Maximum a Posterior (MAP) formulation was proposed to integrate image registration into blind image Super-Resolution (SR) reconstruction to reduce image registration errors.
为了减小配准误差对盲超分辨率重建的影响，提出了一种影像配准和盲超分辨率重建联合处理的模型与方法。
Compressed video Super-resolution(SR)technique estimates High-resolution(HR)images from a sequence of Low-resolution(LR)observations, it has been a great focus for video Super-resolution.
压缩视频超分辨率（SR）技术是利用 压缩后的低分辨率（LR）图像序列来 重建高分辨率（HR）图像的技术，是当前 视频超分辨率技术研究的热点。
Compressed video Super-resolution(SR)technique estimates High-resolution(HR)images from a sequence of Low-resolution(LR)observations, it has been a great focus for video Super-resolution.
压缩视频超分辨率（SR）技术是利用 压缩后的低分辨率（LR）图像序列来 重建高分辨率（HR）图像的技术，是当前 视频超分辨率技术研究的热点。
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