Deep learning optical image denoising research based on principal component estimation

Abstract

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.

Design and Analysis of Orthogonal Polarization Point Diffraction Pinhole Plate

“…Figure 12 shows the interferograms captured in the experiments. After denoising the interferogram images shown in Figure 12 by using the BM3D denoising algorithm [22], the four-step phase-shifting algorithm was used for phase extraction. I0°, I45°, I90°, and I135° represent the interference intensities captured by the polarized camera under four-step phase shifting.…”

Adaptive double PCA method of phase extraction in fringe projection profilometry

“…By exploiting the low-rankness of the fringe order map and sparse nature of the impulsive fringe order errors, Zhang and Xi et al 29 developed a robust principal component analysisbased approach to remove the impulsive fringe order errors. Lu et al 30 presented a fast and highquality denoising algorithm for optical interference images, in which PCA is utilized to analyze image noise and establishes an accurate mapping between estimated noise levels and true noise levels.…”