Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis
This addresses noise issues in PSP and potentially other high-dimensional time-series data, but it is incremental as it builds on existing MSSA and SVD techniques.
The study tackled noise reduction in unsteady pressure-sensitive paint (PSP) measurement data by proposing a method combining multivariate singular spectrum analysis (MSSA) with low-dimensional projection, showing it outperforms truncated SVD in reducing random noise and is less sensitive to truncation rank.
Time-series data, such as unsteady pressure-sensitive paint (PSP) measurement data, may contain a significant amount of random noise. Thus, in this study, we investigated a noise-reduction method that combines multivariate singular spectrum analysis (MSSA) with low-dimensional data representation. MSSA is a state-space reconstruction technique that utilizes time-delay embedding, and the low-dimensional representation is achieved by projecting data onto the singular value decomposition (SVD) basis. The noise-reduction performance of the proposed method for unsteady PSP data, i.e., the projected MSSA, is compared with that of the truncated SVD method, one of the most employed noise-reduction methods. The result shows that the projected MSSA exhibits better performance in reducing random noise than the truncated SVD method. Additionally, in contrast to that of the truncated SVD method, the performance of the projected MSSA is less sensitive to the truncation rank. Furthermore, the projected MSSA achieves denoising effectively by extracting smooth trajectories in a state space from noisy input data. Expectedly, the projected MSSA will be effective for reducing random noise in not only PSP measurement data, but also various high-dimensional time-series data.