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文件名称:去噪到压缩感知
文件大小:416KB
文件格式:DOC
更新时间:2022-04-24 05:49:15
压缩感知 去噪
从去噪到压缩感知论文。Image denoising based on compressed sensing、Abstract—A denoising algorithm seeks to remove noise, errors,
or perturbations from a signal. Extensive research has been
devoted to this arena over the last several decades, and as a
result, todays denoisers can effectively remove large amounts
of additive white Gaussian noise. A compressed sensing (CS)
reconstruction algorithm seeks to recover a structured signal
acquired using a small number of randomized measurements.
Typical CS reconstruction algorithms can be cast as iteratively
estimating a signal from a perturbed observation. This paper
answers a natural question: How can one effectively employ a
generic denoiser in a CS reconstruction algorithm? In response,
we develop an extension of the approximate message pass-
ing (AMP) framework, called denoising-based AMP (D-AMP),
that can integrate a wide class of denoisers within its iterations.
We demonstrate that, when used with a high-performance
denoiser for natural images, D-AMP offers the state-of-the-
art CS recovery performance while operating tens of times
faster than competing methods. We explain the exceptional
performance of D-AMP by analyzing some of its theoretical
features. A key element in D-AMP is the use of an appropriate
Onsager correction term in its iterations, which coerces the signal
perturbation at each iteration to be very close to the white
Gaussian noise that denoisers are typically designed to remove.
Index Terms—Compressed sensing, denoiser, approximate
message passing, Onsager correction.