Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms us...Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low accuracy.In this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error.The sparsity estimation method is combined with the construction of the support set based on sensing dictionary.Using the adaptive sparsity method,an iterative signal reconstruction algorithm is proposed.The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.According to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.展开更多
Many different effective reflection information are often contaminated by exterior and random noise which concealed in the seismic data.Traditional single or fixed transform is not suit for exploiting their complicate...Many different effective reflection information are often contaminated by exterior and random noise which concealed in the seismic data.Traditional single or fixed transform is not suit for exploiting their complicated characteristics and attenuating the noise.Recent years,a novel method so-called morphological component analysis(MCA)is put forward to separate different geometrical components by amalgamating several irrelevance transforms.According to study the local singular and smooth linear components characteristics of seismic data,we propose a method of suppressing noise by integrating with the advantages of adaptive K-singular value decomposition(K-SVD)and wave atom dictionaries to depict the morphological features diversity of seismic signals.Numerical results indicate that our method can dramatically suppress the undesired noises,preserve the information of geologic body and geological structure and improve the signal-to-noise ratio of the data.We also demonstrate the superior performance of this approach by comparing with other novel dictionaries such as discrete cosine transform(DCT),undecimated discrete wavelet transform(UDWT),or curvelet transform,etc.This algorithm provides new ideas for data processing to advance quality and signal-to-noise ratio of seismic data.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supported by the National Natural Science Foundation of China(61773202,71874081)the Special Financial Grant from China Postdoctoral Science Foundation(2017T100366)+2 种基金the Key Laboratory of Avionics System Integrated Technology for National Defense Science and Technology,China Institute of Avionics Radio Electronics(6142505180407)the Open Fund of CAAC Key laboratory of General Aviation Operation,Civil Aviation Management Institute of China(CAMICKFJJ-2019-04)the Innovation Project of the Civil Aviation Administration of China(EAB19001)。
文摘Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low accuracy.In this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error.The sparsity estimation method is combined with the construction of the support set based on sensing dictionary.Using the adaptive sparsity method,an iterative signal reconstruction algorithm is proposed.The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.According to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.
基金sponsored by National Natural Science Foundation of China(No.41672325,41602334)National Key Research and Development Program of China(No.2017YFC0601505).
文摘Many different effective reflection information are often contaminated by exterior and random noise which concealed in the seismic data.Traditional single or fixed transform is not suit for exploiting their complicated characteristics and attenuating the noise.Recent years,a novel method so-called morphological component analysis(MCA)is put forward to separate different geometrical components by amalgamating several irrelevance transforms.According to study the local singular and smooth linear components characteristics of seismic data,we propose a method of suppressing noise by integrating with the advantages of adaptive K-singular value decomposition(K-SVD)and wave atom dictionaries to depict the morphological features diversity of seismic signals.Numerical results indicate that our method can dramatically suppress the undesired noises,preserve the information of geologic body and geological structure and improve the signal-to-noise ratio of the data.We also demonstrate the superior performance of this approach by comparing with other novel dictionaries such as discrete cosine transform(DCT),undecimated discrete wavelet transform(UDWT),or curvelet transform,etc.This algorithm provides new ideas for data processing to advance quality and signal-to-noise ratio of seismic data.
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.
