Liyong Ma

Monte Carlo simulation of spectral reflectance and BRDF of the bubble layer in the upper ocean.

The presence of bubbles can significantly change the radiative properties of seawater and these changes will affect remote sensing and underwater target detection. In this work, the spectral reflectance and bidirectional reflectance characteristics of the bubble layer in the upper ocean are investigated using the Monte Carlo method. The Hall-Novarini (HN) bubble population model, which considers the effect of wind speed and depth on the bubble size distribution, is used. The scattering coefficients and the scattering phase functions of bubbles in seawater are calculated using Mie theory, and the inherent optical properties of seawater for wavelengths between 300 nm and 800 nm are related to chlorophyll concentration (Chl). The effects of bubble coating, Chl, and bubble number density on the spectral reflectance of the bubble layer are studied. The bidirectional reflectance distribution function (BRDF) of the bubble layer for both normal and oblique incidence is also investigated. The results show that bubble populations in clear waters under high wind speed conditions significantly influence the reflection characteristics of the bubble layer. Furthermore, the contribution of bubble populations to the reflection characteristics is mainly due to the strong backscattering of bubbles that are coated with an organic film.

Photoacoustic imaging method based on arc-direction compressed sensing and multi-angle observation

In photoacoustic imaging (PAI), the photoacoustic (PA) signal can be observed only from limit-view angles due to some structure limitations. As a result, data incompleteness artifacts appear and some image details lose. An arc-direction mask in PA data acquisition and arc-direction compressed sensing (CS) reconstruction algorithm are proposed instead of the conventional rectangle CS methods for PAI. The proposed method can effectively realize the compression of the PA data along the arc line and exactly recover the PA images from multi-angle observation. Simulation results demonstrate that it has the potential of application in high-resolution PAI for obtaining highly resolution and artifact-free PA images.

Photoacoustic image reconstruction based on Bayesian compressive sensing algorithm

The photoacoustic tomography (PAT) method, based on compressive sensing (CS) theory, requires that, for the CS reconstruction, the desired image should have a sparse representation in a known transform domain. However, the sparsity of photoacoustic signals is destroyed because noises always exist. Therefore, the original sparse signal cannot be effectively recovered using the general reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is employed to obtain highly sparse representations of photoacoustic images based on a set of noisy CS measurements. Results of simulation demonstrate that the BCS-reconstructed image can achieve superior performance than other state-of-the-art CS-reconstruction algorithms.

Mitigating end effects of EMD using non-equidistance grey model

Aiming at mitigating end effects of empirical mode decomposition (EMD), a new approach motivated by the non-equidistance grey model (NGM) termed as NGM(1,1) is proposed. Other than trapezoid formulas, the cubic Hermite spline is put forward to improve the accuracy of derivative to the accumulated generating operation (AGO) series. Hopefully, it is worth stressing that the proposed NGM(1,1) model is particularly useful for predicting uncertainty data. Qualitative and quantitative comparisons between the proposed approach and other well-known algorithms are carried out through computer simulations on synthetic as well as natural signals. Simulation results demonstrate the proposed method can reduce end effects and improve the decomposition results of EMD.