Microseismic noise suppression is widely used in the exploration of unconventional oil and gas resources. The effective microseismic downhole signals have extremely weak energy are contaminated by strong interference, making data processing interpretation difficult. need for high-frequency signal reservation presents a basic problem design methods. represent as continuous reflection event more ...

Model-based image denoising methods are well suited for use as processors in remote sensing systems such satellites due to their well-developed mathematical theory and low computational cost, but these can often only deal with a single type of random noise. In this paper, based on the model-based techniques, mixed noise algorithm using Gaussian curvature surface shearlet transform is proposed. ...

In this paper we introduce a new localization framework for wavelet transforms, such as the 1D wavelet transform and the Shearlet transform. Our goal is to design nonadaptive window functions that promote sparsity in some sense. For that, we introduce a framework for analyzing localization aspects of window functions. Our localization theory diverges from the conventional theory in two ways. Fi...

introduction in recent years methods based on radio frequency waves have been used for detecting breast cancer. using theses waves leads to better results in early detection of breast cancer comparing with conventional mammography which has been used during several years. materials and methods in this paper, a new method is introduced for detection of backscattered signals which are received by...

Although the conventional image processing methods can detect fabric defects, defect detection is an open research problem due to diversity of types. In this paper, feasibility VGG16 deep learning architecture for has been demonstrated. A new database used. The pre-trained model on built. Thus, training time reduced. experimental results show that outperforms traditional Shearlet transform and ...