Reduction of speckle noise from optical coherence tomography images using multi-frame weighted nuclear norm minimization method

In this paper, we propose a speckle noise reduction method for spectral-domain optical coherence tomography (SD-OCT) images called multi-frame weighted nuclear norm minimization (MWNNM). This method is a direct extension of weighted nuclear norm minimization (WNNM) in the multi-frame framework since an adequately denoised image could not be achieved with single-frame denoising methods. The MWNNM method exploits multiple B-scans collected from a small area of a SD-OCT volumetric image, and then denoises and averages them together to obtain a high signal-to-noise ratio B-scan. The results show that the image quality metrics obtained by denoising and averaging only five nearby B-scans with MWNNM method is considerably better than those of the average image obtained by registering and averaging 40 azimuthally repeated B-scans.     

Analysis of Quality of Services in LTE and Mobile WiMAX

Third Generation Partnership Project Long Term Evolution (3GPP LTE) and Worldwide Interoperability for Microwave Access (WiMAX) face challenges in providing Quality of Services (QoS). In this Paper, the Quality of Services of Mobile WiMAX and 3GPP LTE are analyzed and compared. We analyze the LTE and WiMAX performances in downlink employing an analytical approach for estimating the Quality of Services based on its parameters including delay and bit error rate (BER). Our proposed approach consists of two steps. The physical layer is simulated using the finite state Markov channel (FSMC) model to calculate the bit error rate. Then, the data link layer is simulated employing the effective bandwidth model for both constant and variable rate traffics. Our analysis demonstrates that WiMAX provides better QoS in low average signal to noise ratio (SNR). However, by increasing the average signal to noise ratio, both technologies provide almost the same QoS.     

Less is more: compressive sensing in optics and image science

We review an emerging sampling theory, namely compressive sampling, which reproduces signals or images from a much smaller set of samples than that required by the Nyquist–Shannon criterion. This review article outlines the main theoretical concepts surrounding compressive sensing and discusses the relationship among compressive sensing, signal sparsity, and sensing modalities. We also describe the applications of compressive sensing in the field of optics and image science.