FUNDAMENTAL LIMITS OF IMAGE DENOISING: ARE WE THERE YET?

被引：6

作者：

Chatterjee, Priyam ^{[1
]}

Milanfar, Peyman ^{[1
]}

机构：

[1] Univ Calif Santa Cruz, Dept Elect Engn, Santa Cruz, CA 95064 USA

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2010年

关键词：

Image denoising; estimation; Bayesian Cramer-Rao lower bound; mean squared error; LEARNED DICTIONARIES; SPARSE;

D O I：

10.1109/ICASSP.2010.5495447

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In this paper, we study the fundamental performance limits of image denoising where the aim is to recover the original image from its noisy observation. Our study is based on a general class of estimators whose bias can be modeled to be affine. A bound on the performance in terms of mean squared error (MSE) of the recovered image is derived in a Bayesian framework. In this work, we assume that the original image is available, from which we learn the image statistics. Performances of some current state-of-the-art methods are compared to our MSE bounds for some commonly used experimental images. These show that some gain in denoising performance is yet to be achieved.

引用

页码：1358 / 1361

页数：4

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