Abstract
We describe and demonstrate an optimization-based X-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement.
Highlights
Most image reconstruction problems can be categorized as inverse problem solving
Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to and fly-scan ptychographic tomography
We have shown that Adorym provides a reconstruction framework for several imaging methods, including multi-distance near-field holography (MDH), sparse multislice ptychography (SMP) and 2D ptychography, and projection as well as ptychographic tomography
Summary
Most image reconstruction problems can be categorized as inverse problem solving. One begins with the assumption that a measurable set of data y arises from a forward model F (x, θ), which in turn depends on an object function x and parameters θ, giving y = F (x, θ). (1)With experimental data ymeas and additive noise , imaging experiments build a relation of ymeas = F (x, θ) + . (2)When F is a non-linear function of x, or when the problem size is very large, a direct solution of Eq 2 is either intractable or non-existent at all. A common example is standard filtered-backprojection tomography, which is known to produce significant artifacts when the projection images are sparse in viewing angles [1]. These issues motivate the use of iterative methods in solving Eq 2, where x is gradually adjusted to find a minimum of a loss function L that is often formulated as. Letting x be a complex methods such as far-field ptychography [4, 5] and its near-field counterpart [6], the probe function might itself be finite sized but unknown, but it can be recovered along with the object as part of recovering x. In other methods such as holotomography different Fresnel [7], at each object rotational propagation distances from angle one the object xm,igahntdheaavcehamseeat soufrienmteennstitmy imghetashuarveemeexnptesryimmeeanstaacl qeurriorerds at in position or rotation, collectively symbolized by θ
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