An optical Amplitude and Pulse Position Modulation(APPM) mapping scheme for strong turbulent atmospheric channel is proposed to optimize Bit Error Rate(BER) performance.In this scheme,a nonequidifferent amplitude seri...An optical Amplitude and Pulse Position Modulation(APPM) mapping scheme for strong turbulent atmospheric channel is proposed to optimize Bit Error Rate(BER) performance.In this scheme,a nonequidifferent amplitude series is designed based on quantitative BER analysis of the specific A×M APPM demapping procedures containing time slot selection and amplitude decision in selected time slot,which are different from traditional ones.Simulation results of 4×4,4×8 and 4×16 APPM show 4,3.4 and 6.9 d B SNR gain against traditional APPM scheme respectively.Thus significant BER performance improvement is achieved which helps to enhance reliability of freespace optical communication systems.展开更多
Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artif...Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.展开更多
基金financial supports from National High Technology 863 Program of China(No.2012AA011304)National International Technology Cooperation(No.2012DFG12110)+5 种基金National NSFC(No.61275158/61201151/61275074)Beijing Nova Program( No.Z141101001814048)Beijing Excellent Ph.D.Thesis Guidance Foundation(No.20121001302)the Universities Ph.D.Special Research Funds(No.20120005110003)the Fundamental Research Funds for the Central Universities with No.2014RC0203Fund of State Key Laboratory of IPOC(BUPT)
文摘An optical Amplitude and Pulse Position Modulation(APPM) mapping scheme for strong turbulent atmospheric channel is proposed to optimize Bit Error Rate(BER) performance.In this scheme,a nonequidifferent amplitude series is designed based on quantitative BER analysis of the specific A×M APPM demapping procedures containing time slot selection and amplitude decision in selected time slot,which are different from traditional ones.Simulation results of 4×4,4×8 and 4×16 APPM show 4,3.4 and 6.9 d B SNR gain against traditional APPM scheme respectively.Thus significant BER performance improvement is achieved which helps to enhance reliability of freespace optical communication systems.
文摘Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.
