ﻻ يوجد ملخص باللغة العربية
For 3D Synthetic Aperture Radar (SAR) imaging, one typical approach is to achieve the cross-track 1D focusing for each range-azimuth pixel after obtaining a stack of 2D complex-valued images. The cross-track focusing is the main difficulty as its aperture length is limited and the antenna positions are usually non-uniformly distributed. Sparsity regularization methods are widely used to tackle these problems. However, these methods are of obvious limitations. The most well-known ones are their heavy computational burdens and unsatisfied stabilities. In this letter, an efficient deep network-based cross-track imaging method is proposed. When trained, the imaging process, i.e. the forward propagation of the network, is made up of simple matrix-vector calculations and element-wise nonlinearity operations, which significantly speed up the imaging. Also, we find that the deep network is of good robustness against noise and model errors. Comprehensive simulations and experiments have been carried out, and the superiority of the proposed method can be clearly seen.
Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less sp
High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical information important for clinical application and quantitative image analysis. However, HR MRI conventionally comes at the cost of longer scan time, smaller spatial cov
Extending super-resolution imaging techniques to objects hidden in strongly scattering media potentially revolutionize the technical analysis for much broader categories of samples, such as biological tissues. The main challenge is the medias inhomog
Computed Tomography (CT) imaging technique is widely used in geological exploration, medical diagnosis and other fields. In practice, however, the resolution of CT image is usually limited by scanning devices and great expense. Super resolution (SR)
Super-resolution imaging with advanced optical systems has been revolutionizing technical analysis in various fields from biological to physical sciences. However, many objects are hidden by strongly scattering media such as rough wall corners or bio