Book description
The concept of an inverse problem is a familiar one to most scientists
and engineers, particularly in the field of signal and image processing,
imaging systems (medical, geophysical, industrial non-destructive
testing, etc.) and computer vision. In imaging systems, the aim is not
just to estimate unobserved images, but also their geometric
characteristics from observed quantities that are linked to these
unobserved quantities through the forward problem. This book focuses on
imagery and vision problems that can be clearly written in terms of an
inverse problem where an estimate for the image and its geometrical
attributes (contours and regions) is sought.
The chapters of this book use a consistent methodology to examine
inverse problems such as: noise removal; restoration by deconvolution;
2D or 3D reconstruction in X-ray, tomography or microwave imaging;
reconstruction of the surface of a 3D object using X-ray tomography or
making use of its shading; reconstruction of the surface of a 3D
landscape based on several satellite photos; super-resolution; motion
estimation in a sequence of images; separation of several images mixed
using instruments with different sensitivities or transfer functions;
and more.
