Sampling theorems on the sphere state that all the information of a continuous band-limited signal on the sphere may be contained in a discrete set of samples. For an equiangular sampling of the sphere, the Driscoll & Healy (DH) [1] sampling theorem has become the standard, requiring ∼ 4L samples on the sphere to represent exactly a signal band-limited in its spherical harmonic decomposition at...

INTRODUCTION In interventional MRI (I-MRI), a sequence of MR images is reconstructed in order to guide a diagnostic or therapeutic procedure where an invasive device is inserted in the body. The need for near-real-time image updates places two distinct constraints on I-MRI reconstruction: 1) high frame rate (several frames per second, depending on the application [2,3]); 2) causal reconstructio...

This paper describes the development of a sub-Nyquist sampling system that can digitize high-speed signals using a low-speed analog to digital converter (ADC). The system is implemented by a field programmable gate array (FPGA), and it is possible to make change to the equivalent sampling frequency according to the practical applications. As an application of the compressed sensing (CS) theory,...

Introduction: The main idea of Compressed Sensing is to exploit the fact that there is some structure and redundancy in most signals of interest. Clearly, the more we known about the signal and the more the information we encode into the signal processing algorithm, the better performance we can achieve. In this paper, we propose an adaptive compressed MRI sensing scheme that combined wavelet e...

In recent years compressive sampling (CS) has appeared in the signal processing literature as a legitimate contender for processing of sparse signals. Natural signals such as speech, image and video are compressible. In most signal processing systems dealing with these signals the signal is first sampled and later on compressed. The philosophy of CS however is to sample and compress the signal ...

we give some new results on sparse signal recovery in the presence of noise, forweighted spaces. traditionally, were used dictionaries that have the norm equal to 1, but, forrandom dictionaries this condition is rarely satised. moreover, we give better estimationsthen the ones given recently by cai, wang and xu.