Super-resolution reconstruction technology is to explore new information between the under-sampling image series
obtained from the same scene and to achieve the high-resolution picture through image fusion in sub-pixel level. The
traditional super-resolution fusion methods for sub-sampling images need motion estimation and motion interpolation
and construct multi-resolution pyramid to obtain high-resolution, yet the function of the human beings' visual features
are ignored. In this paper, a novel resolution reconstruction for under-sampling images of static scene based on the
human vision model is considered by introducing PCNN (Pulse Coupled Neural Network) model, which simplifies and
improves the input model, internal behavior and control parameters selection. The proposed super-resolution image
fusion algorithm based on PCNN-wavelet is aimed at the down-sampling image series in a static scene. And on the basis
of keeping the original features, we introduce Relief Filter(RF) to the control and judge segment to overcome the effect
of random factors(such as noise, etc) effectively to achieve the aim that highlighting interested object though the fusion.
Numerical simulations show that the new algorithm has the better performance in retaining more details and keeping
high resolution.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.