Description
HIO is an iterative method that gradually improves the reconstruction by imposing known properties of the image as constraints. HIO starts with a set of random phases as initial guess, then applies IFT and FT to the image to move it back and forth between real and Fourier spaces. Typically, the constraint for the real space is the fact the image has only a finite support, while the constraint in the Fourier space is that the magnitude of the Fourier image should be close to that measured. Figure 1(a) shows a graph representation of the process.
(a) Schematic of the conventional HIO algorithm. The highlighted part indicates the
constraint to be studied and improved. (b) Proposed image processing pipeline.
Alternating Direction Methods for Hybrid Input Output (HIO) algorithm
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