Robust Single-Shot Fringe Projection Profilometry Based on Morphological Component Analysis

Abstract

In a fringe projection profilometry (FPP) process, the captured fringe images can be modeled as the superimposition of the projected fringe patterns on the texture of the objects. Extracting the fringe patterns from the captured fringe images is an essential procedure in FPP; but traditional single-shot FPP methods often fail to perform if the objects have a highly textured surface. In this paper, a new single-shot FPP algorithm which allows the object texture and fringe pattern to be estimated simultaneously is proposed. The heart of the proposed algorithm is an enhanced morphological component analysis (MCA) tailored for FPP problems. Conventional MCA methods which use a uniform threshold in an iterative optimization process are inefficient to separate fringe-like patterns from image texture. We extend the conventional MCA by taking advantage of the low-rank structure of the fringe's sparse representation to enable an adaptive thresholding process. It ends up with a robust single-shot FPP algorithm that can extract the fringe pattern even if the object has a highly textured surface. The proposed approach has a side benefit that the object texture can be simultaneously obtained in the fringe pattern estimation process, which is useful in many FPP applications. Experimental results have demonstrated the improved performance of the proposed algorithm over the conventional single-shot FPP approaches.

A Novel SAR Imaging Method Based on Morphological Component Analysis

“…Comparing the two different clutter dictionaries, MCA-SAR-KSVD achieves a better clutter and noise suppression effect than MCA-SAR-DCT, which means that K-SVD has provided a better clutter dictionary through the adopted iterative training process. To illustrate the clutter and noise suppression performance in a quantitative way, the SCR and BSF are used to evaluate the imaging results, and they are defined as follows [49] (27) where, 𝜇 𝑡 and 𝜇 𝑐 denote the average gray value of the target and non-target areas, respectively, 𝜎 𝑐 is the standard deviation of gray values in non-target areas, and 𝜎 𝑖𝑛 represents that of the non-target areas in the image obtained by RDA. As it is difficult to distinguish noise from the background clutter, the suppression ability for clutter and noise is evaluated together.…”

“…MCA is a signal decomposition method based on sparse representation, which uses different dictionaries to decompose the signal into different components containing different types of information [27].…”

Super-Resolution Phase Retrieval Network for Single-Pattern Structured Light 3D Imaging

“…By using a two-step decoding process consisting of color decoding and geometric decoding, this method achieved high-quality 3D reconstruction. Budianto et al [34] proposed a robust color-coded single-pattern SL method. They implemented an enhanced morphological component analysis method to separate texture and fringe patterns from a single RGB fringe pattern, and this method achieved better performance than the traditional single-pattern methods.…”

Structured light three-dimensional reconstruction method based on GFU-Net network

“…The traditional fringe projection contouring is roughly divided into two categories, the first one is phase shift contouring (PSP), which is able to achieve high-resolution phase measurement by taking multiple fringe images with relative phase difference for phase extraction, but it is easy to be subjected to external interference, and difficult to be applied in dynamic measurements [5]. The second category is the single-frame fringe analysis method represented by Fourier transform contouring (FTP), which achieves phase information extraction using only a single fringe image, but it is difficult to deal with the discontinuous jumps between the phases of two neighbouring point parcels [6]. Compared with PSP, single-frame FPP can acquire 3D information at one time, which is very suitable for measuring moving objects or dynamic 3D reconstruction, but its accuracy is lower than that of PSP.Therefore, whether it is possible to quickly and accurately reconstruct the 3D morphology of an object from a single-frame fringe pattern has become an important area of FPP research [7].…”