Mingjian Sun

Kernel Sparse Multitask Learning for Hyperspectral Image Classification With Empirical Mode Decomposition and Morphological Wavelet-Based Features

Recently, many researchers have attempted to exploit spectral-spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral-spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral-spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples.

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.

Feature selection and classification algorithm for non-destructive detecting of high-speed rail defects based on vibration signals

Many rail accidents were caused by rail defects, therefore the detection of the rail defects is of vital importance. Using simulated and experimental measurements, the rail defect detection was carried out. The feature parameters were extracted both from time domain and time-frequency domain. Then the sequential backward selection method was applied to select the important feature parameters. After optimizing of the feature parameter set, support vector machine method was applied to recognize and classify the rail defects. It has been proved that the proposed algorithm of analyzing and processing the rail defect vibration signals is an effective and non-destructive detecting method of the rail defects.

PHOTOACOUSTIC SIGNALS DENOISING BASED ON EMPIRICAL MODE DECOMPOSITION AND ENERGY-WINDOW METHOD

In the process of photoacoustic imaging (PAI), the photoacoustic signals are polluted by a strong background white noise, which is caused by many factors such as the system thermal noise or short noise, the tissue reflecting or scattering interference, and the impedance match lack between the transducer and tissue. The inevitable noise can degrade the contrast sensitivity of photoacoustic images seriously. In this paper, based on the energy window, a CMSE-EMD denoising method is employed to photoacoustic image reconstruction. Results of the simulation demonstrate that it can eliminate the image artifacts more effectively and achieve great improvement in image quality.

Photoacoustic microscopy image resolution enhancement via directional total variation regularization

Photoacoustic microscopy (PAM) is recognized as a powerful tool for various microcirculation system studies. To improve the spatial resolution for the PAM images, the requirements of the system will always be increased correspondingly. Without additional cost of the system, we address the problem of improving the resolution of PAM images by integrating a deconvolution model with a directional total variation regularization. Additionally, we present a primal-dual-based algorithm to solve the associated optimization problem efficiently. Results from both test images and some PAM images studies validate the effectiveness of the proposed method in enhancing the spatial resolution. We expect the proposed technique to be an alternativeresolution enhancement tool for some important biomedical applications.

Compressive sampling photoacoustic tomography based on edge expander codes and TV regularization

A new photoacoustic (PA) signal sampling and image reconstruction method, called compressive sampling PA tomography (CSPAT), is recently proposed to make low sampling rate and high-resolution PA tomography possible. A key problem within the CSPAT framework is the design of optic masks. We propose to use edge expander codes-based masks instead of the conventional random distribution masks, and efficient total variation (TV) regularization-based model to formulate the associated problem. The edge expander codes-based masks, corresponding to non-uniform sampling schemes, are validated by both theoretical analysis and results from computer simulations. The proposed method is expected to enhance the capability of CSPAT for reducing the number of measurements and fast data acquisition.

Compressive Sampling Photoacoustic Microscope System based on Low Rank Matrix Completion

Abstract Photoacoustic Microscopy (PAM) has developed into a powerful tool for deep tissue imaging with a better spatial resolution. But the data acquisition time in PAM is so long that it is a great challenge for real time imaging. In this paper, a new PAM data acquisition and image recovery method, called Compressive Sampling PAM System based on Low Rank Matrix Completion (CSLRM-PAM) is proposed to obtain a high-resolution PAM image with relatively low sampling rates. In order to successfully set up a CSLRM-PAM system, the two key problems which we need to keep focus on are design of the compressive sampling scheme and the corresponding image recovery algorithm. In this paper, two compressive sampling schemes based on expander graphs are proposed to replace the conventional point-by-point scanning scheme to implement fast data acquisition. Then, the low rank matrix completion is utilized to obtain high-resolution PAM image directly from the compressive sampling data. The effectiveness of the proposed scheme is validated using both numerical analysis and PAM experiments. In contrast with the conventional system, the proposed CSLRM-PAM system is able to dramatically decrease the total sampling points for a relatively high-resolution PAM image and to implement accelerated data acquisition.

Non-destructive photoacoustic detecting method for high-speed rail surface defects

The rail defect detection is vital to the rapid development of high-speed rail. A new, easily available nondestructive testing method which applies the photoacoustic detection technology is proposed here for the rail defect detection. Based on the ultrasonic sensor, a real-time photoacoustic imaging system for the rail non-destructive testing is established. The model of the system and its numerical solution are built first, and then the corresponding experiments and analysis are conducted. The photoacoustic image with damage characteristics can be reconstructed through the photoacoustic signals which excited by pulsed laser and then reflected back from the rail surface. According to the reconstructed image, the damage information such as the appearance, extension trend, and the depth of the rail defect can be effectively identified. It has been proved that the established photoacoustic detecting method for high-speed rail surface detection is an effective and non-destructive detecting technology.

Single-image super-resolution using directional total variation regularization and alternating direction method of multiplier solver

Abstract. Single-image super-resolution (SR) is one of the most important and challenging issues in image processing. To produce a high-resolution image from a low-resolution image, one of the conventional approaches is to leverage regularization to overcome the limitations caused by the modeling. However, conventional regularizers such as total variation always neglect the high-level structures in the data. To overcome the drawback, we propose to explore the underlying information for the images with structured edges by using directional total variation. An alternating direction method of a multiplier-based algorithm is presented to effectively solve the resulting optimization problem. Computer simulations on several texture images such as a leaf image have been used to demonstrate the effectiveness and improvement of the proposed method on SR reconstruction, both qualitatively and quantitatively. Furthermore, the effect of parameter selection is also discussed for the proposed method.