Infrared Dim Target Detection Using Shearlet’s Kurtosis Maximization under Non-Uniform Background

A novel method based on multiscale and multidirectional feature fusion in the shearlet transform domain and kurtosis maximization for detecting the dim target in infrared images with a low signal-to-noise ratio (SNR) and serious interference caused by a cluttered and non-uniform background is presented in this paper. First, an original image is decomposed using the shearlet transform with translation invariance. Second, various directions of high-frequency subbands are fused and the corresponding kurtosis of fused image is computed. The targets can be enhanced by strengthening the column with maximum kurtosis. Then, processed high-frequency subbands on different scales of images are merged. Finally, the dim targets are detected by an adaptive threshold with a maximum contrast criterion (MCC). The experimental results show that the proposed method has good performance for infrared target detection in comparison with the nonsubsampled contourlet transform (NSCT) method.

PARAMETRIC BLIND IMAGE DEBLURRING WITH GRADIENT BASED SPECTRAL KURTOSIS MAXIMIZATION

Blind image deconvolution/deblurring (BID) is a challenging task due to lack of prior information about the blurring process and image. Noise and ringing artefacts resulted during the restoration process further deter fine restoration of the pristine image. These artefacts mainly arise from using a poorly estimated point spread function (PSF) combined with an ineffective restoration filter. This paper presents a BID scheme based on the steepest descent in kurtosis maximization. Assuming uniform blur, the PSF can be modelled by a parametric form. The scheme tries to estimate the blur parameters by maximizing kurtosis of the deblurred image. The scheme is devised to handle any type of blur that can be framed into a parametric form such as Gaussian, motion and out-of-focus. Gradients for the blur parameters are computed and optimized in the direction of increasing kurtosis value using a steepest descent scheme. The algorithms for several common blurs are derived and the effectiveness has been corroborated through a set of experiments. Validation has also been carried out on various real examples. It is shown that the scheme optimizes on the parameters in a close vicinity of the true parameters. Results of both benchmark and real images are presented. Both full-reference and non-reference image quality measures have been used in quantifying the deblurring performance. The results show that the proposed method offers marked improvements over the existing methods.

Gearbox Fault Diagnosis of Wind Turbine by KA and DRT

The spectral kurtosis analysis (KA) is used to select the filter parameters (FPs) combined with the application of the demodulation resonance technique (DRT) for a gearbox fault diagnosis (FD) of wind turbine. Based on the proposed method, the FPs can be selected automatically according to the kurtosis maximization principle. By changing of the shaft speed under the variable loads conditions, the natural frequency (NF) of the gearbox will be shifted and will affect the accuracy of the detection of the faults. So, the effect of the external loads on the NF of the gearbox is examined based on the simulation of the gearbox. In addition, the fast kurtogram (FK) combined with the demodulated resonance technology is used to process the simulated faulty signal of a gearbox. The results show that the FD of the gearbox is modified by correcting the NF shifts due to the variation of the operating loads.

Minimizing PAPR and Synchronization Errors in OFDM for WiMAX Using Software Defined Radio

This paper addresses the reduction of peak-to-average power ratio (PAPR) and synchronization errors of an orthogonal frequency division multiplexing (OFDM) for Mobile-WiMAX physical layer (PHY) standard. In the process, the best achievable PAPR of 0 dB with efficient power amplification is found for the OFDM signal using phase modulation technique, which avoids the nonlinear distortion. Further, the constant modulus algorithm (CMA) and sign kurtosis maximization adaptive algorithm (SKMAA) equalizers are used in the system to reduce the synchronization errors. However, the experimental study is performed on a test platform for a practical proof of the concept. The test platform is based on the Ettus universal software radio peripheral (USRP) N210 hardware and the GNU Radio open source software. Several tests are carried out to observe the effect of equalizers on the system. The performance comparison of bit error rate/symbol error rate (BER/SER) values are tabulated for the system with and without equalization.