Danhua Liu

UWB Echo Signal Detection With Ultra-Low Rate Sampling Based on Compressed Sensing

A major challenge in ultra-wide-band (UWB) signal processing is the requirement for very high sampling rate. The recently emerging compressed sensing (CS) theory makes processing UWB signal at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on the CS theory, a system for sampling UWB echo signal at a rate much lower than Nyquist rate and performing signal detection is proposed in this paper. First, an approach of constructing basis functions according to matching rules is proposed to achieve sparse signal representation because the sparse representation of signal is the most important precondition for the use of CS theory. Second, based on the matching basis functions and using analog-to-information converter, a UWB signal detection system is designed in the framework of the CS theory. With this system, a UWB signal, such as a linear frequency-modulated signal in radar system, can be sampled at about 10% of Nyquist rate, but still can be reconstructed and detected with overwhelming probability. The simulation results show that the proposed method is effective for sampling and detecting UWB signal directly even without a very high-frequency analog-to-digital converter.

Color demosaicking with sparse representations

Color demosaicking is an ill-posed inverse problem of image restoration. The performance of a color demosaicking algorithm depends on how thoroughly it can exploit domain knowledge to confine the solution space for the underlying true color image. We propose a sparsity-based ℓ1 minimization technique for color demosaicking that exploits both interband and intra-band sparse representations of natural images. In some of most challenging cases of color demosaicking, the proposed technique outperforms those published in the literature by a significant margin in both PSNR and visual quality.

High-Resolution Imaging Via Moving Random Exposure and Its Simulation

In this correspondence, we introduce a new imaging method to obtain high-resolution (HR) images. The image acquisition is performed in two stages, compressive measurement and optimization reconstruction. In order to reconstruct HR images by a small number of sensors, compressive measurements are made. Specifically, compressive measurements are made by a low-resolution (LR) camera with randomly fluttering shutter, which can be viewed as a moving random exposure pattern. In the optimization reconstruction stage, the HR image is computed by different models according to the prior knowledge of scenes. The proposed imaging method offers a new way of acquiring HR images of essentially static scenes when the camera resolution is limited by severe constraints such as cost, battery capacity, memory space, transmission bandwidth, etc. and when the prior knowledge of scenes is available. The simulation results demonstrate the effectiveness of the proposed imaging method.

A robust image encryption scheme over wireless channels

In traditional image encryption system, decryption is extremely sensitive to packet loss. However, in wireless networks, packet loss is inevitable. Compressed sensing (CS) theory shows that sparse signal can be recovered from few incomplete measurements of it. Strong randomness of measurement matrix and irrelevance among the elements of the measurement vector imply that measurement process can be regarded as encryption process. So, this paper, based on CS theory, presents a new image encryption scheme with robustness to packet loss. In the scheme, we design a Gaussian random measurement matrix as the key to realize data encryption. Moreover, to enhance the incoherence between the plain-image and the cipher-image, we add a random disturbance term to the measurements (cipher-image) and thus improve the security level of the cipher-image. Numerical experiments show that the proposed method not only has well anti-attack ability but also is robust to packet loss, which can still decrypt plain-image even when the packet loss ratio is up to 50%.

High-resolution multispectral imaging with random coded exposure

Abstract For multispectral image acquisition in remote sensing, high spatial resolution requires a small instantaneous field of view (IFOV). However, the smaller the IFOV, the lower the amount of light exposure to imaging sensors, and the lower the signal-to-noise ratio. To overcome this weakness, we propose a new random coded exposure technique for acquiring high-resolution multispectral images without reducing IFOV. The new image acquisition system employs a high-speed rotating mirror controlled by a random sequence to modulate exposure to an ordinary imager without increasing the sampling rate. The proposed high-speed coded exposure strategy makes it possible to maintain sufficient light exposure even with a small IFOV. The randomly sampled multispectral image can be recovered in high spatial resolution by exploiting the signal sparsity. The recovery algorithm is based on the compressive sensing theory. Simulation results demonstrate the efficacy of the proposed technique.

High-resolution spectral imaging based on coded dispersion

Since the energy of the incident light is constant, the spatial and spectral resolution can hardly be improved without scarifying the other with the spectral imaging method of a pushbroom scanner. Thus, a new spectral imaging method is proposed to obtain a high-resolution (HR) spectral image with a low-resolution detector array. The method, namely coded dispersion, by which compressive measurement is achieved, improves light collection efficiency, and then a high-quality reconstructed HR spectral image is obtained with fewer sensors. The simulation result shows that with prior knowledge of scenes available, the proposed method also offers a new way to acquire an HR spectral image while the density of detector array is constrained by battery, capacity, transmission bandwidth, and cost.

Compressive sensing-based multiple description image coding for wireless channel

A new theory, known as Compressive Sensing (CS), has recently proved that sparse or compressible signal can be accurately reconstructed from very highly undersampled data by solving by solving a convex LI optimization problem. This paper, based on such new theory, presents a novel multiple description coding (MDC) method to combat packet loss. According to compressive sensing framework, our method has an attractive property that the reconstruction error only depends on the received measurement number but not on which measurements are received. Another advantage of our method is that it is a balanced MDC scheme with fine description granularity and low encoding complexity.

A new multiple description image coding scheme based on Compressive Sensing

This paper proposes a new two-description image coding technique based on Compressive Sensing(CS). In the new multiple description coding scheme, the source image is split into two sub-images with quincunx downsampling operation and further two descriptions are generated by CS measurement. The decoding is accomplished by solving a convex l1 optimization problem. The decoding at each side decoder is done by an interpolation process that exploits inter-description correlation. The new method still makes use of an attractive property of CS that the recovery effect only depends on the received measurement number but not on which measurements are received correctly. Experimental results demonstrate that the proposed image multiple description coding scheme has the advantage of high robustness to packet loss.

High resoluton image reconstruction: A new imager via movable random exposure

Recently compressive sensor developed as an imager for capturing images effectively has been studied extensively. In this paper, we design a new imager to reconstruct high resolution image from a low resolution blurred image obtained by the intended movable random exposure. This imager grabs an image by moving a camera with a randomly fluttering shutter along a certain motion route. By analyzing this kind of movable random exposure process, we find it can be considered as compressive sampling described in the compressive sensing (CS) theory. Then according to the CS theory, the exposure result of this imager can be used to recover a high resolution image. Since this imager consists only a movable camera and a fluttered shutter, it is relatively simple and easy to implement. The simulation results show that the proposed imager can recover high even ultra-high resolution images with good reconstruction performance.

A new method for signal sparse decomposition

Increasing attention has been paid to the signal sparse representation based on overcomplete dictionaries in the fields of signal processing. The sparsity degree of decomposed signal is directly related with the dictionary chosen, and the computation speed depends on the sparse decomposition algorithm and the scale of dictionary. Almost all sparse decomposition algorithms available suffer from enormous computational complexity, which severely affects the practicability of these algorithms and limits the development of sparse representation upon an overcomplete dictionary. This paper presents a new method for decomposing a signal upon overcomplete dictionary. This method first constructs a special concatenate dictionary with several orthogonal bases and presents a iterative group matching search algorithm. The experiments results show that our algorithm can reduce the computation burden greatly and is more efficient than MP. This paper also proposes a method to determine a near optimal value of the total number of coefficients.