Hyperspectral Acquisition Technology Based on Compressed Sampling in Spatial Domain

In the hyperspectral imaging device, the sensor detects the reflection or radiation intensity of the target at hundreds of different wavelengths, thus forming a spectral image composed of hundreds of continuous bands. The traditional processing method of sampling first and then compressing not only cannot fundamentally solve the problem of huge amount of data, but also causes waste of resources. To solve this problem, a spectral image reconstruction method based on compressed sampling in spatial domain and transform coding in spectral domain is designed by using the sparsity of single-band two-dimensional image and the spectral redundancy of spatial coded data. Based on Bayesian theory, a compressed sensing measurement matrix of adaptive projection is proposed. Combining these two algorithms, an adaptive Grouplet-FBCS algorithm is constructed to reconstruct the image using smooth projection Landweber. Experimental results show that, compared with existing image block compression sensing algorithms, this algorithm can significantly improve the quality of image signal reconstruction.

Generalized deep iterative reconstruction for sparse-view CT imaging

Sparse-view CT is a promising approach in reducing the X-ray radiation dose in clinical CT imaging. However, the CT images reconstructed from the conventional filtered backprojection (FBP) algorithm suffer from severe streaking artifacts. Iterative reconstruction (IR) algorithms have been widely adopted to mitigate these streaking artifacts, but they may prolong the CT imaging time due to the intense data-specific computations. Recently, model-driven deep learning (DL) CT image reconstruction method, which unrolls the iterative optimization procedures into the deep neural network, has shown exciting prospect in improving the image quality and shortening the reconstruction time. In this work, we explore the generalized unrolling scheme for such iterative model to further enhance its performance on sparse-view CT imaging. By using it, the iteration parameters, regularizer term, data-fidelity term and even the mathematical operations are all assumed to be learned and optimized via the network training. Results from the numerical and experimental sparse-view CT imaging demonstrate that the newly proposed network with the maximum generalization provides the best reconstruction performance.

A Full-Polarization Radar Image Reconstruction Method with Orthogonal Coding Apertures

Imaging radar is widely applied in both military and civil fields, including remote sensing. In recent years, polarization information has attracted more and more attention in the imaging radar. The orthogonality between different channels is always the core problem for the full-polarization imaging radar. To solve this problem, an image reconstruction method using orthogonal coding apertures technique is proposed for full-polarization imaging radar in this paper. Firstly, the signal model of the orthogonal coding apertures is proposed. This model realizes the ideal time-domain orthogonality between switching two channels by the apertures with two trains of orthogonal codes. Then, a multichannel joint reconstruction method based on compressed sensing is proposed for the imaging processing, which is named the coded aperture simultaneous orthogonal matching pursuit (CAS-OMP) algorithm. The proposed algorithm combines the information of all polarization channels so as to ensure the consistency of the scattering point position obtained by each polarization channel and also improves the reconstruction accuracy. Finally, the simulation experiments using both the simple scaled model of the satellite and measured data of an unmanned aerial vehicle (UAV) are conducted, and the effectiveness of the proposed method is verified.

The Influence of Noise in the Super-Resolution Reconstruction of Structured Illumination Microscopy

As one of the mainstream super-resolution imaging technologies, structured illumination microscopy (SIM) is popular for its fast imaging speed and simple optical path structure. Spectrum separation is a key step in the reconstruction of super-resolution images. However, in the process of imaging, the unavoidable noise will seriously affect the accuracy of frequency spectrum separation. This paper carries out a simulation study on the influence of noise in the process of frequency spectrum separation. The results show that although noise can cause distortion of low-frequency information in frequency spectrum separation results, it has little influence on high-frequency information. Therefore, a super-resolution image reconstruction method is proposed to effectively suppress the influence of noise. Both simulation and experimental results are shown the method can suppress the influence of noise without losing the details of super-resolution.

Generalized Image Reconstruction in Optical Coherence Tomography Using Redundant and Non-Uniformly-Spaced Samples

In this paper, we use Frame Theory to develop a generalized OCT image reconstruction method using redundant and non-uniformly spaced frequency domain samples that includes using non-redundant and uniformly spaced samples as special cases. We also correct an important theoretical error in the previously reported results related to OCT image reconstruction using the Non-uniform Discrete Fourier Transform (NDFT). Moreover, we describe an efficient method to compute our corrected reconstruction transform, i.e., a scaled NDFT, using the Fast Fourier Transform (FFT). Finally, we demonstrate different advantages of our generalized OCT image reconstruction method by achieving (1) theoretically corrected OCT image reconstruction directly from non-uniformly spaced frequency domain samples; (2) a novel OCT image reconstruction method with a higher signal-to-noise ratio (SNR) using redundant frequency domain samples. Our new image reconstruction method is an improvement of OCT technology, so it could benefit all OCT applications.