Reconstruction methods in image processing
Image reconstruction techniques are used to create 2-D and 3-D images from sets of 1-D projections. These reconstruction techniques form the basis for common imaging modalities such as CT, MRI, and PET, and they are useful in medicine, biology, earth science, archaeology, materials science, and nondestructive testing.
The mathematical foundation for these reconstruction methods are the Radon transform, the inverse Radon transform, and the projection slice theorem. Computational techniques include filtered backprojection and a variety of iterative methods. Several projection geometries are commonly used, including parallel beam, fan beam, and cone beam. The Shepp-Logan phantom image is often used to evaluate different reconstruction algorithms.
An effective approach to performing image reconstruction includes using methods in a technical computing environment for data analysis, visualization, and algorithm development. See Image Processing Toolbox™ for more information. Read Image Reconstruction related MathWorks Stories.
Examples and How To
See also: image recognition, image transform, image enhancement, image segmentation, image processing and computer vision, feature extraction, stereo vision, optical flow, color profile, image analysis, image thresholding, edge detection, image registration, affine transformation, lab color, point cloud, 3D Image Processing