No abstract

A good orthogonality is provided by the wavelet-based orthogonality frequency division multiplexing (W-OFDM) and well reduction of bit error rate is gained with W-OFDM over conventional OFDM systems. Most importantly, it provides enhanced spectral efficiency since it does not require cyclic prefix (CP) which leads to the achievement of ultra-high data rates. Hence, this can be used in 5 th generation (5G) wireless communication networks. It is proposed to use the discrete wavelet transform (DWT) based OFDM in place of conventional discrete fourier transform (DFT) based OFDM in the applications of long term evaluation (LTE). However, discrete wavelet transforms (DWT) based OFDM system suffers from the down sampling, which mitigates the actual size of input bit streams and there by degradation in the system efficiency. To resolve this issue, and for enhancing further mitigation in bit error rate (BER) performance, a novel W-OFDM is proposed. Our proposed W-OFDM system utilizes translation invariant wavelet transform (TIWT) in place of DWT for enhanced spectral efficiency. In addition, fading channel environment is considered to disclose the effectiveness of proposed TIWT-OFDM system. Extensive experimental results show that the BER performance of proposed TIWT-OFDM is superior to the existing OFDM systems like DFT-OFDM and DWT-OFDM systems.

This article presents an effective method for image denoising using non-local means with packing of multi patches (NLM-PMP) in non-subsampled contourlet (NSC) domain which works based on multi-scale decomposition and directionality. Guided filter with image statistics (SGIF) is utilized to further process the obtained denoised image for mitigation of ringing artifacts those are invariably appeared, which leads to the local structure preservation like textures, edges and small details efficiently. The authors decomposed the image into low-pass subband coefficients and bandpass subband coefficients including the base and the detail scales. The low frequency noise in the base sub-band and the edges with small textural details in the detail scale are processed independently using an NLM-PMP filter. In addition, SGIF is applied to enhance the visual perception of denoised image. Experimental results indicate that the proposed approach is competitive at lower noise strength with respect to peak signal to noise ratio and structural similarity index measure and excels in performance at higher noise strength compared with several state-of-the-art algorithms.