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[Books] Compressivsampling DL : 0: Compressive sampling-Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired resolution of the image, i.e. the number of pixels in the image. This paper surveys an emerging theory which goes by the name of “compressive sampling” or “compressed sensing,” and which says that this conventional wisdom is inaccurate. Perhaps surprisingly, it is possible to reconstruct images or signals of scientific interest accurately and sometimes even exactly from a number of samples which is far smaller than the desired resolution of the image/signal, e.g. the number of pixels in the image
Date : 2025-12-30 Size : 110kb User : yjsdqq
[Books] Anintroduciontocompressivesampling DL : 0: An Introduction To Compressive Sampling-Conventional approaches to sampling signals or images follow Shannon’s celebrated theorem: the sampling rate must be at least twice the maximum frequency present in the signal (the so-called Nyquist rate). In fact, this principle underlies nearly all signal acquisition protocols used in consumer audio and visual electronics, medical imaging devices, radio receivers, and so on. (For some signals, such as images that are not naturally bandlimited, the sampling rate is dictated not by the Shannon theorem but by the desired temporal or spatial resolution. However, it is common in such systems to use an antialiasing low-pass filter to bandlimit the signal before sampling, and so the Shannon theorem plays an implicit role.) In the field of data conversion, for example, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation: the signal is uniformly sampled at or above the Nyquist rate
Date : 2025-12-30 Size : 1.09mb User : yjsdqq
[Books] FASTCOMPRESSIVESAMPLINGWITHSTRUCTURALLYRANDOMMATRI DL : 0: FAST COMPRESSIVE SAMPLING WITH STRUCTURALLY RANDOM MATRICES
Date : 2025-12-30 Size : 138kb User : yjsdqq