Comparison of PUMA and CUNWRAP to 2-D phase unwrapping

Phase unwrapping (PU) is the process of recovering the absolute phase φ from the wrapped phase ψ. PU is the one of the most important step of the interferometry SAR processing for calculation of DEM, and can be applied to other field, such as MRI medical image. This paper presents comparison two algorithms for two dimensional phase unwrapping based on network programming, namely PUMA and CUNWRAP. The PUMA (Phase Unwrapping Maximum Flow), PUMA has first order Markov Random Fields used for the energy minimization framework. PUMA algorithm solves integer optimization problems, by computing a sequence of binary optimizations, solved by graph cuts techniques for PU max-flow/min-cut. PUMA is the minimum L^p norm or Global class of PU problem. CUNWRAP (Constantini unwrapping) is a phase unwrapping method that exploits globally the integer qualities of the problem. CUNWRAP method leads to formulating the PU model as the problem of minimizing the weighted deviations between the estimated and the unknown neighboring pixel differences of the unwrapped phase with the constraint that the deviation must be integer multiple of 2π. CUNWRAP used as network structure and makes it possible to employ very efficient strategies (linear programming method) for its solution. The formulation of phase unwrapping problem by CUNWRAP is a global minimization problem with integer variable. The two algorithms applied to test performed on simulated and real image (interferometric SAR data and magnetic resonance imaging). Ratio of elapsed time between CUNWRAP and PUMA, range for simulation image test is 1.6 to 9.09 times, while range for real image test is 4 to 37 times. Based on Peak Signal to Noise Ratio (PSNR), effect of noise for simulated data, view of unwrapping result, elapsed time, show that PUMA is better than CUNWRAP globally.