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We address the problem of resolving two closely spaced complex-valued points from N irregular Fourier domain samples. Although this is a generic super-resolution problem, our target application is SAR tomography where typically the number of acquisitions is N = 10…100 and SNR = 0…10dB. In this paper, a compressive sensing based algorithm is introduced for tomographic SAR inversion. It is named “Scale-down...
Resolution is a crucial aspect for urban imaging where structures are in the same spatial scale as the resolution of the imaging instrument. This is particularly true for 3D SAR imaging, also referred to as SAR Tomography (TomoSAR). We address the problem of super-resolution (SR), i.e. the ability to resolve two closely spaced complex-valued points from N irregular Fourier domain samples. Our target...
The requirements for better resolution in all medical imaging modalities currently represent a very important and open challenge. Accurate measurement and visualization of structure in living tissues is intrinsically limited by the imaging system features. Imaging beyond these limits in medical imaging is referred to as super-resolution. We provide first a brief overview of super-resolution imaging...
In this paper, a selective perceptual-based (SELP) scheme is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual Human Visual System (HVS) model is proposed to compute the contrast sensitivity threshold for a given background intensity. The obtained thresholds are used to select which pixels...
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