Phase unwrapping method improves noise handling in imaging tiles
Translation-Invariant Tile-Based Phase Unwrapping with Residual-Weighted Multipath Averaging
Computer Vision and Pattern Recognition
Summary
Phase unwrapping is like trying to figure out the full shape of a wave when you only see parts of it wrapped around in circles. This is important in imaging techniques that use wave patterns, but it is tricky because noise and real breaks in the image can confuse the process. The authors created a method that unwraps small sections (tiles) of the image using a frequency technique, then carefully combines them by averaging over shifted grids and weighting by error measures to avoid seams. Their method helps reduce errors caused by noise or edges and matches or beats existing approaches on tests.
What this means in practice
- •For remote sensing engineers: Improve phase retrieval steps in radar and satellite images with better noise tolerance and edge handling.
- •For medical imaging teams: Enhance phase unwrapping in MRI or ultrasound scans to reduce artifacts caused by noise and discontinuities.
Authors
Antoine Moevus, Max Mignotte
Abstract
Phase unwrapping is a key step in interferometric and coherent imaging, where the physical quantity of interest is carried by a phase that the instrument delivers only modulo 2*pi. The difficulty in two dimensions is to separate the jumps caused by wrapping from those produced by noise, by true discontinuities, by under-sampling or by decorrelation. Spatial-domain and frequency-domain methods have both been studied extensively, each with advantages the other lacks; hybrid schemes combining the two remain scarce. We propose a semi-global tile-based strategy in which every tile is unwrapped in the frequency domain, through the Discrete Cosine Transform (DCT) and the least squares (LS) formalism of Ghiglia et al., the tiles being merged spatially. Unwrapping and noise filtering are performed jointly, which regularizes an otherwise ill-posed inverse problem. An error stays confined to the tile in which it arose; the tiling artifacts are removed by averaging over every shift of the grid and over the symmetries of the square; and that average is weighted by the Poisson residual each pass leaves behind, so that a pass whose tile boundaries fell on a discontinuity does not impose its seam on the result. Experiments on synthetic and real data, against four reference algorithms from four distinct families and under six complementary metrics, show that the proposed method matches or improves on the state of the art. One of these metrics, a corrected cyclic re-wrap residual introduced here, needs no ground truth and therefore remains available on real acquisitions.