外文翻譯---圖像去噪技術(shù)研究

1、外文資料SURVEY OF IMAGE DENOISING TECHNIQUESMurkesh C. Motwani Image Process Technology, Inc. 1776 Back Country Road Reno, NV 89521 USA (775) 448-7816 mukesh@image-process.comMurkesh C. Gadiya University of Pune, India V

2、ishwakarma Inst. of Tech. Pune 411337, INDIA 91-9884371488 mukesh_gadiya@satyam.comRakhi C. Motwani University of Nevada, Reno Dept of Comp. Sci. & Engr.Reno, NV 89557 USA (775) 853-7897Frederick C. Harris, Jr.Univ

3、ersity of Nevada, Reno Dept of Comp. Sci. & Engr.,Reno, NV 89557 USA (775) 784-6571AbstractRemoving noise from the original signal is still a challenging problem for researchers. There have been several published

4、algorithms and each approach has its assumptions, advantages, and limitations. This paper presents a review of some significant work in the area of image de-noising. After a brief introduction, some popular approaches ar

5、e classified into different groups and an overview of various algorithms and analysis is provided. Insights and potential future trends in the area of de-noising are also discussed.1. IntroductionDigital images play an i

6、mportant role both in daily life applications such as satellite television, magnetic resonance imaging, computer tomography as well as in areas of research and technology such as geographical information systems and astr

7、onomy. Data sets collected by image sensors are generally contaminated by noise. Imperfect instruments, problems with the data acquisition process, and interfering natural phenomena can all degrade the data of interest.

8、Furthermore, noise can be introduced by transmission errors and compression. Thus, de-noising is often a necessary and the first step to be taken before the images data is analyzed. It is necessary to apply an efficient

9、de-noising technique to compensate for such data corruption.Image de-noising still remains a challenge for researchers because noise removal introduces artifacts and causes blurring of the images. This paper describes di

10、fferent methodologies for noise reduction (or de-noising) giving an insight as to which statistical properties of the wavelet coefficients and its neighbors. Future trend will be towards finding more accurate probabilist

11、ic models for the distribution of non-orthogonal wavelet coefficients.3. Classification of De-noising AlgorithmsAs shown in Figure 1, there are two basic approaches to image de-noising, spatial filtering methods and tran

12、sform domain filtering methods.3.1 Spatial FilteringA traditional way to remove noise from image data is to employ spatial filters. Spatial filters can be further classified into non-linear and linear filters.I. Non-Line

13、ar FiltersWith non-linear filters, the noise is removed without any attempts to explicitly identify it. Spatial filters employ a low pass filtering on groups of pixels with the assumption that the noise occupies the high

14、er region of frequency spectrum. Generally spatial filters remove noise to a reasonable extent but at the cost of blurring images which in turn makes the edges in pictures invisible. In recent years, a variety of nonline

15、ar median- type filters such as weighted median, rank conditioned rank selection, and relaxed median have been developed to overcome this drawback.II. Linear FiltersA mean filter is the optimal linear filter for Gaussian

16、 noise in the sense of mean square error. Linear filters too tend to blur sharp edges, destroy lines and other fine image details, and perform poorly in the presence of signal-dependent noise. The wiener filtering method

17、 requires the information about the spectra of the noise and the original signal and it works well only if the underlying signal is smooth. Wiener method implements spatial smoothing and its model complexity control corr

18、espond to choosing the window size. To overcome the weakness of the Wiener filtering, Donoho and Johnstone proposed the wavelet based denoising scheme in.3.2 Transform Domain FilteringThe transform domain filtering metho

19、ds can be subdivided according to the choice of the basis functions. The basis functions can be further classified as data adaptive and non-adaptive. Non-adaptive transforms are discussed first since they are more popul