This book is devoted to the issue of image
super-resolution-obtaining high-resolution images from single or
multiple low-resolution images. Although there are numerous
algorithms available for image interpolation and super-resolution,
there's been a need for a book that establishes a common thread
between the two processes. Filling this need, Image
Super-Resolution and Applications presents image interpolation as a
building block in the super-resolution reconstruction process.
Instead of approaching image interpolation as either a
polynomial-based problem or an inverse problem, this book breaks
the mold and compares and contrasts the two approaches. It presents
two directions for image super-resolution: super-resolution with a
priori information and blind super-resolution reconstruction of
images. It also devotes chapters to the two complementary steps
used to obtain high-resolution images: image registration and image
fusion. Details techniques for color image interpolation and
interpolation for pattern recognition Analyzes image interpolation
as an inverse problem Presents image registration methodologies
Considers image fusion and its application in image super
resolution Includes simulation experiments along with the required
MATLAB (R) code Supplying complete coverage of image-super
resolution and its applications, the book illustrates applications
for image interpolation and super-resolution in medical and
satellite image processing. It uses MATLAB (R) programs to present
various techniques, including polynomial image interpolation and
adaptive polynomial image interpolation. MATLAB codes for most of
the simulation experiments supplied in the book are included in the
appendix.
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