Modified wavelet-domain spatiotemporal Wiener filtering and motion-detection algorithms are employed to facilitate video enhancement, motion-activity-based indexing and summarization.
As shown in the previous section, the proposed method newly adopts the following two novel approaches: (i) Simultaneous resolution enhancement and motion blur removal The proposed method uses the posterior probability for simultaneously estimating the HR frame and the motion blur kernels from the target motion blurred LR frames.
Simultaneous resolution enhancement and motion blur removal.
Then, by using the Maximum a Posterior (MAP) estimation, the proposed method performs the simultaneous resolution enhancement and motion blur removal.
This article presents an SR method for realizing the simultaneous resolution enhancement and motion blur removal based on adaptive prior settings.
A super-resolution method for simultaneously realizing resolution enhancement and motion blur removal based on adaptive prior settings are presented in this article.
As shown in the above discussions, the conventional methods have the following problems: (i) simultaneous resolution enhancement and motion blur removal cannot be realized, successfully, and (ii) regularization, i.e., prior information cannot be provided for target video sequences, adaptively.
Other energy-saving enhancements included motion and daylight detectors to turn lights off when not needed, and special exterior windows which automatically dim to prevent daytime heating.
In order to obtain high-resolution (HR) video sequences from motion-blurred low-resolution video sequences, both of the resolution enhancement and the motion blur removal have to be performed.
Furthermore, the improved detective quantum efficiency of the detectors (Ruskin et al., 2013; McMullan et al., 2014) has enhanced the signal present in the images, and this enhancement combined with motion correction has enabled single-particle reconstructions to reach higher resolutions.
