Efficient and robust phase unwrapping method based on SFNet.

Phase unwrapping is a crucial step in obtaining the final physical information in the field of optical metrology. Although good at dealing with phase with discontinuity and noise, most deep learning-based spatial phase unwrapping methods suffer from the complex model and unsatisfactory performance, partially due to simple noise type for training datasets and limited interpretability. This paper proposes a highly efficient and robust spatial phase unwrapping method based on an improved SegFormer network, SFNet. The SFNet structure uses a hierarchical encoder without positional encoding and a decoder based on a lightweight fully connected multilayer perceptron. The proposed method utilizes the self-attention mechanism of the Transformer to better capture the global relationship of phase changes and reduce errors in the phase unwrapping process. It has a lower parameter count, speeding up the phase unwrapping. The network is trained on a simulated dataset containing various types of noise and phase discontinuity. This paper compares the proposed method with several state-of-the-art deep learning-based and traditional methods in terms of important evaluation indices, such as RMSE and PFS, highlighting its structural stability, robustness to noise, and generalization.

Error analysis and realization of a phase-modulated diffraction grating used as a displacement sensor.

A grating-based interferometric cavity produces coherent diffraction light field in a compact size, serving as a promising candidate for displacement measurement by taking advantage of both high integration and high accuracy. Phase-modulated diffraction gratings (PMDGs) make use of a combination of diffractive optical elements, allowing for the diminishment of zeroth-order reflected beams and thus improving the energy utilization coefficient and sensitivity of grating-based displacement measurements. However, conventional PMDGs with submicron-scale features usually require demanding micromachining processes, posing a significant challenge to manufacturability. Involving a four-region PMDG, this paper establishes a hybrid error model including etching error and coating error, thus providing a quantitative analysis of the relation between the errors and optical responses. The hybrid error model and the designated process-tolerant grating are experimentally verified by micromachining and grating-based displacement measurements using an 850 nm laser, confirming the validity and effectiveness. It is found the PMDG achieves an energy utilization coefficient (the ratio of the peak-to-peak value of the ±1st order beams to the 0th-order beam) improvement of nearly 500% and a four-fold reduction in 0th-order beam intensity compared with the traditional amplitude grating. More importantly, this PMDG maintains very tolerant process requirements, and the etching error and coating error can be up to 0.5 µm and 0.6 µm, respectively. This offers attractive alternatives to the fabrication of PMDGs and grating-based devices with wide process compatibility. This work first systematically investigates the influence of fabrication errors and identifies the interplay between the errors and the optical response for PMDGs. The hybrid error model allows further avenues for the fabrication of diffraction elements with practical limitations of micromachining fabrication.

Deep learning optical image denoising research based on principal component estimation.

High-quality denoising of optical interference images usually requires preliminary prediction of the noise level. Although blind denoising can filter the image at the pixel level without noise prediction, it inevitably loses a significant amount of phase information. This paper proposes a fast and high-quality denoising algorithm for optical interference images that combines the merits of a principal component analysis (PCA) and residual neural networks. The PCA is used to analyze the image noise and, in turn, establishes an accurate mapping between the estimated and true noise levels. The mapping helps to select a suitable residual neural network model for image processing, which maximizes the retention of image information and reduces the effect of noise. In addition, a comprehensive evaluation factor to account for the time complexity and denoising effect of the algorithm is proposed, since time complexity can be a dominant concern in some cases of actual measurement. The performance of the denoising algorithm and the effectiveness of the evaluation criterion are demonstrated to be high by processing a set of optical interference images and benchmarking with other denoising algorithms. The proposed algorithm outperforms the previously reported counterparts in a specific area of optical interference image preprocessing and provides an alternative paradigm for other denoising problems of optics, such as holograms and structured light measurements.