Understanding the Gerchberg-Saxton Algorithm through Convex Optimization

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The Gerchberg-Saxton algorithm is used to retrieve the phase of a wave from a diffraction pattern when the object's dimensions are unknown, addressing the "phase problem." The algorithm operates through an iterative process that alternates between the diffraction plane and the object plane, refining the phase estimate with each iteration. Understanding the mathematical foundation of the algorithm can be enhanced through resources like the suggested ResearchGate paper, which connects the algorithm to convex optimization principles. This iterative method aims to converge on a solution that accurately represents the phase of the wave. Mastery of the Gerchberg-Saxton algorithm is crucial for applications in imaging and signal processing.
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Please help me understand the Gerchberg–Saxton algorithm

what I comprehended so far

1) A source is shone on an object and produces a diffraction pattern in the diffraction plane. We do not know the dimensions of the object.

2) We are unable to calculate the phase of the of the wave, due to the "phase problem"

Now we want to computationally find the phase of the wave that is produced in the diffraction plane


I do not understand how the Gerchberg–Saxton algorithm works. i.e. how we improve to get a closer phase through the iterative method

Thanks
 
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If you have checked Wikipedia and your doubts are still not clear, trying reading this paper available freely at ResearchGate:

https://www.researchgate.net/publication/11281090_Phase_retrieval_Gerchberg-Saxton_algorithm_and_Fienup_variants_A_view_from_convex_optimization
 
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