Lifting dual tree complex wavelets transform

We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denoising, segmentation, image fusion and compression. The results of applications shrinkage denoising demonstrate objective and subjective enhancements over the dual tree complex wavelet transform (DTCWT). The results of the shrinkage denoising example application indicate empirical and subjective enhancements over the DTCWT. The new transform with the DTCWT provide a trade-off between denoising computational competence of performance, and memory necessities. We tend to use the PSNR (peak signal to noise ratio) alongside the structural similarity index measure (SSIM) and the SSIM map to estimate denoised image quality

A Two-stage approach to harmonic rejection mixing using blind interference cancelling

Current analog harmonic rejection mixers typically provide 30–40 dB of harmonic rejection, which is often not sufficient. We present a mixed analog-digital approach to harmonic rejection mixing that uses a digital interference canceler to reject the strongest interferer. Simulations indicate that, given a practical RF scenario, the digital canceler is able to improve the signal-to-interference ratio by 30–45 dB

A flexible heterogeneous video processor system for television applications

A new video processing architecture for high-end TV applications is presented, featuring a flexible heterogeneous multi-processor architecture, executing video tasks in parallel and independently. The signal flow graph and the processors are programmable, enabling an optimal picture quality for different TV display modes. The concept is verified by an experimental chip design. The architecture allows several video streams to be processed and displayed in parallel and in a programmable way, with an individual signal qualit

A Multitaper-Random Demodulator Model for Narrowband Compressive Spectral Estimation

The random demodulator (RD) is a compressive sensing (CS) system for acquiring and recovering bandlimited sparse signals, which are approximated by multi-tones. Signal recovery employs the discrete Fourier transform based periodogram, though due to bias and variance constraints, it is an inconsistent spectral estimator. This paper presents a Multitaper RD (MT-RD) architecture for compressive spectrum estimation, which exploits the inherent advantage of the MT spectral estimation method from the spectral leakage perspective. Experimental results for sparse, narrowband signals corroborate that the MT-RD model enhances sparsity so affording superior CS performance compared with the original RD system in terms of both lower power spectrum leakage and improved input noise robustness