Artificial Intelligence in Digital Holographic Imaging

4Phase Unwrapping

Phase information associated with a fringe pattern in an interferogram from digital holographic microscopy (DHM) is calculated by shifting the fringe through different known phase increments or by Fourier transforming the fringe pattern, which is obtained by adding a considerable tilt to the wave front causing carrier fringes [1, 2]. In either case, the phase distribution of a phase image means that principal values are wrapped in a range of −π to π, which can cause 2π phase jumps due to phase periodicity (with a phase modulus of 2π) of trigonometric functions. A phase unwrapping process must be conducted to remove 2π phase discontinuities in the image and obtain an estimate of the true continuous phase image. Phase unwrapping consists of detecting the location of the phase jump then connecting adjacent pixels by adding or subtracting multiples of 2π to remove phase discontinuities.

Many phase unwrapping algorithms were proposed to solve challenging problems such as phase discontinuities. Phase unwrapping algorithms can be generally grouped into three major categories: global algorithms, region algorithms, and path‐following algorithms [3, 4]. Global algorithms minimize differences between discrete gradients of wrapped and unwrapped phase images [5–11]. The L_P‐norm and least‐squares algorithms are typical examples of this category. Although these algorithms are generally robust, their computational requirements are huge, making them unsuitable for real‐time, ...