Understanding Kernel Size in Blind Deconvolution

Most blind deconvolution methods usually pre-define a large kernel size to guarantee the support domain. Blur kernel estimation error is likely to be introduced, yielding severe artifacts in deblurring results. In this paper, we first theoretically and experimentally analyze the mechanism to estimation error in oversized kernel, and show that it holds even on blurry images without noises. Then to suppress this adverse effect, we propose a low rank-based regularization on blur kernel to exploit the structural information in degraded kernels, by which larger-kernel effect can be effectively suppressed. And we propose an efficient optimization algorithm to solve it. Experimental results on benchmark datasets show that the proposed method is comparable with the state-of-the-arts by accordingly setting proper kernel size, and performs much better in handling larger-size kernels quantitatively and qualitatively. The deblurring results on real-world blurry images further validate the effectiveness of the proposed method.Comment: Accepted by WACV 201

Unambiguous Tracking Method Based on Combined Correlation Functions for sine/cosine-BOC CBOC and AltBOC Modulated Signals

Unambiguous tracking for Binary Offset Carrier (BOC) modulated signals is an important requirement of modern Global Navigation Satellite System (GNSS) receivers. An unambiguous tracking method based on combined correlation functions for even/odd order sine/cosine-BOC, Composite BOC(CBOC) and Alternate BOC(AltBOC) modulated signals is proposed. Firstly, a unitary mathematical formulation for all kinds of BOC modulations is introduced. Then an unambiguous tracking method is proposed based on the formulation and the idea of pseudo correlation function (PCF) method. Finally, the tracking loop based on the proposed method is designed. Simulation results indicate that the proposed method can remove side peaks while retaining the sharp main peak for all kinds of BOC modulations. The tracking performance for AltBOC is examined and the results show that the proposed method has better performance in thermal noise and long-delay multipath mitigation than the traditional unambiguous tracking methods

Linear-Combined-Code-Based Unambiguous Code Discriminator Design for Multipath Mitigation in GNSS Receivers

Unambiguous tracking and multipath mitigation for Binary Offset Carrier (BOC) signals are two important requirements of modern Global Navigation Satellite Systems (GNSS) receivers. A GNSS discriminator design method based on optimization technique is proposed in this paper to meet these requirements. Firstly, the discriminator structure based on a linear-combined code is given. Then the requirements of ideal discriminator function are converted into the mathematical constraints and the objective function to form a non-linear optimization problem. Finally, the problem is solved and the local code is generated according to the results. The theoretical analysis and simulation results indicate that the proposed method can completely remove the false lock points for BOC signals and provide superior multipath mitigation performance compared with traditional discriminator and high revolution correlator (HRC) technique. Moreover, the proposed discriminator is easy to implement for not increasing the number of correlators

Ultrafast initialization and QND-readout of a spin qubit via control of nanodot-vacuum coupling

Ultrafast initialization enables fault-tolerant processing of quantum information while QND readout enables scalable quantum computation. By spatially assembling photon resonators and wave-guides around an n-doped nanodot and by temporally designing optical pump pulses, an efficient quantum pathway can be established from an electron spin to a charged exciton to a cavity photon and finally to a flying photon in the waveguide. Such control of vacuum-nanodot coupling can be exploited for ultrafast initialization and QND readout of the spin, which are particularly compatible with the optically driven spin quantum computers.Comment: 4 pages 3 figure