Dept. of Electr. Eng., Rochester Univ., NY.
IEEE Trans Image Process. 1997;6(8):1064-76. doi: 10.1109/83.605404.
Printing from an NTSC source and conversion of NTSC source material to high-definition television (HDTV) format are some of the applications that motivate superresolution (SR) image and video reconstruction from low-resolution (LR) and possibly blurred sources. Existing methods for SR image reconstruction are limited by the assumptions that the input LR images are sampled progressively, and that the aperture time of the camera is zero, thus ignoring the motion blur occurring during the aperture time. Because of the observed adverse effects of these assumptions for many common video sources, this paper proposes (i) a complete model of video acquisition with an arbitrary input sampling lattice and a nonzero aperture time, and (ii) an algorithm based on this model using the theory of projections onto convex sets to reconstruct SR still images or video from an LR time sequence of images. Experimental results with real video are provided, which clearly demonstrate that a significant increase in the image resolution can be achieved by taking the motion blurring into account especially when there exists large interframe motion.
从 NTSC 源打印和将 NTSC 源材料转换为高清电视 (HDTV) 格式是一些应用程序，它们激发了从低分辨率 (LR) 和可能模糊的源进行超分辨率 (SR) 图像和视频重建。现有的 SR 图像重建方法受到以下假设的限制：输入的 LR 图像是按顺序采样的，并且相机的孔径时间为零，从而忽略了在孔径时间期间发生的运动模糊。由于这些假设对许多常见视频源的观察到的不利影响，本文提出了 (i) 具有任意输入采样网格和非零孔径时间的视频采集的完整模型，以及 (ii) 基于该模型的算法，该算法使用投影到凸集的理论从 LR 图像序列重建 SR 静止图像或视频。提供了实际视频的实验结果，这些结果清楚地表明，通过考虑运动模糊，可以显著提高图像分辨率，尤其是当存在大的帧间运动时。
