Convex optimization image recovery

A convex set theoretic image recovery problem involves three steps. 1. Selecting a hilbertian solution space Ξ. 2. Selecting the constraints that yield closed 

What are the basic elements of image recovery?

The following four basic elements are required to solve an image recovery problem: (1) a data formation model, (2) a priori information, (3) a recovery criterion, and (4) a solution method

The recovery criterion defines the class of images that are acceptable as solutions to the problem

What is a convex set theoretic image recovery problem?

For instance, all linear and affine THE CONVEX FEASIBILITY PROBLEM 161 constraints lead to convex sets as well as linear inequality constraints

In addition, a large corpus of nonlinear constraints are of the convex type

A convex set theoretic image recovery problem involves three steps: 1

Selecting a Hilbertian solution space 8; 2

What is iterative convex refinement?

Throughout, convex optimization techniques are shown to be a critically important mathematical tool for imaging science problems and applied extensively

The Iterative Convex Refinement (ICR) aims to solve a hard non-convex optimization problem directly allowing for greater generality in the sparse structure

Convex optimization image recovery
Convex optimization image recovery
Coherent diffractive imaging (CDI) is a lensless technique for 2D or 3D reconstruction of the image of nanoscale structures such as nanotubes, nanocrystals, porous nanocrystalline layers, defects, potentially proteins, and more.
In CDI, a highly coherent beam of X-rays, electrons or other wavelike particle or photon is incident on an object.

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