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
This Letter presents a novel approach to enhance the fringe contrast（visibility） in a digital off-axis hologram digitally, which can save several adjustment procedures. In the approach, we train a pair of coupled di...This Letter presents a novel approach to enhance the fringe contrast（visibility） in a digital off-axis hologram digitally, which can save several adjustment procedures. In the approach, we train a pair of coupled dictionaries from a low fringe contrast hologram and a high one of the same specimen, use the dictionaries to sparse code the input hologram, and finally output a higher fringe contrast hologram. The sparse representation shows good adaptability on holograms. The experimental results demonstrate the benefit of low noise in a three-dimensional profile and prove the effectiveness of the approach.展开更多
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This Letter presents a novel approach to enhance the fringe contrast（visibility） in a digital off-axis hologram digitally, which can save several adjustment procedures. In the approach, we train a pair of coupled dictionaries from a low fringe contrast hologram and a high one of the same specimen, use the dictionaries to sparse code the input hologram, and finally output a higher fringe contrast hologram. The sparse representation shows good adaptability on holograms. The experimental results demonstrate the benefit of low noise in a three-dimensional profile and prove the effectiveness of the approach.
