Qiu-Hua Lin

A blind source separation based method for speech encryption

The underdetermined problem poses a significant challenge in blind source separation (BSS) where the number of the source signals is greater than that of the mixed signals. Motivated by the fact that the security of many cryptosystems relies on the apparent intractability of the computational problems such as the integer factorization problem, we exploit the intractability of the underdetermined BSS problem to present a novel BSS-based speech encryption by properly constructing the underdetermined mixing matrix for encryption, and by generating the key signals that satisfy the necessary condition for the proposed method to be unconditionally secure. Both extensive computer simulations and performance analyses results show that the proposed method has high level of security while retaining excellent audio quality

A blind source separation-based method for multiple images encryption

Blind source separation (BSS) has been successfully applied to many fields such as communications and biomedical engineering. Its application for image encryption, however, remains largely unexplored. In this contribution, a novel BSS-based scheme is proposed for encrypting multiple images, in which the underdetermined BSS problem is fully exploited to achieve the image security. The necessary conditions for generating the key images required for this underdetermined system are presented. The sufficient conditions for constructing the underdetermined mixing matrix for encryption are then described. Extensive computer simulations, coupled with the performance analyses, demonstrate the high level of security of the proposed method.

Speech Segregation Using Constrained ICA

In natural environment, speech often occurs concurrently with acoustic interference. How to effectively extract speech remains a great challenge. This paper describes a novel constrained Independent Component Analysis (ICA) approach, the ICA with reference (ICA-R), to speech segregation. Different from the traditional ICA which recovers simultaneously all the source signals, the ICA-R extracts only some desired source signals from the mixtures of source signals by incorporating some a priori information into the separation process. We show how the ICA-R can be applied to separate a target speech signal from interfering sounds by exploiting a proper reference signal, which is based on the different characteristic between speech signal and its environmental noises, i.e., the speech signal has pitch and its harmonic frequencies whereas the noises usually do not. Results of computer experiments demonstrate the efficiency of the proposed method.

Secure image communication using blind source separation

In this paper, an image encryption method is proposed by using the linear mixing model of blind source separation (BSS). It can simultaneously encrypt multiple images with the same size by mixing them with the same number of statistically independent key images, the size of which is equal to that of the images to be encrypted. Since these multiple images cover mutually through mixing among them while the key images cover them, and there is not any restriction on the key space, the proposed method has high level of security. Computer simulation results show its validity.

A fast decryption algorithm for BSS-Based image encryption

The image encryption based on blind source separation (BSS) takes advantage of the underdetermined BSS problem to encrypt multiple confidential images. Its security can be further improved if the number of images to be simultaneously encrypted increases. However, the BSS decryption speed will correspondingly decrease since the computational load of the BSS algorithms usually has nonlinear relation with the number of the source signals. To solve the problem, this paper presents a fast decryption algorithm based on adaptive noise cancellation by using the knowledge of the key images, which are used in the BSS-based method and available at the receiving side. As a result, the number of the source signals for the fast BSS decryption is decreased in half, and the decryption time is considerably reduced. Both computer simulations and performance analyses demonstrate the efficiency of the proposed method.

Blind source separation-based encryption of images and speeches

Blind source separation (BSS) has been successfully applied in many fields such as communications and biomedical engineering. Its application for image and speech encryption, however, has been scarce. Motivated by the fact that the security of many public-key cryptosystems relies on the apparent intractability of the computational problems such as the integer factorization problem, we present a BSS-based method for encrypting images and speeches by utilizing the BSS underdetermined problem. We discuss how to construct the mixing matrix for encryption and how to generate the key signals. Computer simulation results show that the BSS-based method has high level of security.

A Semi-blind Complex ICA Algorithm for Extracting a Desired Signal Based on Kurtosis Maximization

Semi-blind independent component analysis (ICA) incorporates some prior information into standard blind ICA, and thus solves some problems of blind ICA as well as provides improved performance. However, semi-blind algorithms thus far have been much focused on the separation of real-valued signals but little on separation of complex-valued signals. We propose in this paper a semi-blind complex ICA algorithm for extracting a complex-valued source of interest within the framework of constrained ICA. Specifically, magnitude information about the desired signal is utilized as inequality constraint to the cost function of kurtosis maximization algorithm, which is an efficient complex ICA algorithm for separating circular and noncircular sources. The simulation results demonstrate that the proposed algorithm can extract a desired complex signal with much improved performance and robustness.

Speech enhancement using ICA with EMD-Based reference

Different from the traditional ICA that recovers all the source signals simultaneously, the ICA with reference (ICA-R) extracts only some desired source signals from the mixtures of source signals by incorporating some a priori information into the separation process. This paper applies ICA-R to extracting a target speech signal from its noisy linear mixtures by constructing a proper reference signal with the empirical mode decomposition (EMD). Specifically, EMD is used to obtain an approximate envelope of the power spectrum of the desired speech, which is quite different from the power spectra of the environmental noises. The results of computer simulations and performance analyses demonstrate the efficiency of the proposed method.

Fusion of Laser Point Clouds and Color Images with Post-calibration

Fusion of laser point clouds and color images has a great advantage in the photogrammetry, computer vision, and computer graphics communities. Most of existing methods estimate the projection matrix for mapping laser points to image pixels based on pre-calibration using checkboard patterns. We propose a method with post-calibration based on reference objects. We first generate an ideal projection matrix based on the pinhole camera model and the positional relationship between the laser scanner and the camera. We then correct the projection matrix based on the reference objects having longer vertical edges, as it is easy to detect the edge of the laser points. The horizontal coordinate offset is computed using the laser points and pixels at the vertical edges, and this is finally added to the projection matrix. Our method reduces the complexity of the data fusion. Experimental results verify the correctness of our method.

Image Edge Detection for Stitching Aerial Images with Geometrical Rectification

Changes of the flight attitude of unmanned aircraft cause nonlinear distortion in the aerial images. Stitching these images without geometric rectification may cause the problem of mismatching. However, the geometric rectification model produces invalid regions at the edge of images. A direct cutting for these invalid regions leads to removing lots of useful information, whereas a noisy seam is visible in the stitched image if the edge cutting is omitted. We propose a solution of edge detection for detecting invalid and noisy regions. More precisely, our method includes two passes of edge detection for images with geometric rectification. The first pass of image edge detection is to find the boundary between valid and invalid scenes. The second pass is to detect the noisy regions. The invalid and noisy regions are finally removed for image stitching, and the effective areas of the image are kept to the maximum extent. Experimental results demonstrate the efficacy of the proposed method.