Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images

Performances of Polarization-Retrieve Imaging in Stratified Dispersion Media

We constructed an active imaging model within 10 km of the atmosphere from the satellite to the ground based on Monte Carlo (MC) algorithm, and, because of the inhomogeneous distributions of the scattering particles in atmosphere environment, 10 km atmosphere layer was divided into ten layers in our model. The MC algorithm was used to simulate the transmission process of photons through the atmosphere. By launching lasers of linear polarization states from satellites to ground, the intensity, degree of polarization (DoP), polarization difference (PD), and polarization retrieve (PR) images can be obtained. The contrast of the image, peak signal to noise ratio (PSNR), and structural similarity index (SSI) were used to evaluate the imaging quality. The simulated results demonstrate that the contrast of images is degraded as the atmosphere becomes worse. However, PR imaging have a better contrast and better visibility in different atmospheric conditions. Meanwhile, we found that Mueller matrix (MM) can retrieve the original images very well in a certain range of atmospheric conditions. Finally, the simulation also shows that different wavelengths of light sources have different penetration characteristics, and, in general, infrared light shows better performances than visible light for imaging.

Feature-Level Fusion of Finger Vein and Fingerprint Based on a Single Finger Image: The Use of Incompletely Closed Near-Infrared Equipment

Due to its portability, convenience, and low cost, incompletely closed near-infrared (ICNIR) imaging equipment (mixed light reflection imaging) is used for ultra thin sensor modules and have good application prospects. However, equipment with incompletely closed structure also brings some problems. Some finger vein images are not clear and there are sparse or even missing veins, which results in poor recognition performance. For these poor quality ICNIR images, however, there is additional fingerprint information in the image. The analysis of ICNIR images reveals that the fingerprint and finger vein in a single ICNIR image can be enhanced and separated. We propose a feature-level fusion recognition algorithm using a single ICNIR finger image. Firstly, we propose contrast limited adaptive histogram equalization (CLAHE) and grayscale normalization to enhance fingerprint and finger vein texture, respectively. Then we propose an adaptive radius local binary pattern (ADLBP) feature combined with uniform pattern to extract the features of fingerprint and finger vein. It solves the problem that traditional local binary pattern (LBP) is unable to describe the texture features of different sizes in ICNIR images. Finally, we fuse the feature vectors of ADLBP block histogram for a fingerprint and finger vein, and realize feature-layer fusion recognition by a threshold decision support vector machine (T-SVM). The experimentation results showed that the performance of the proposed algorithm was noticeably better than that of the single model recognition algorithm.