外文翻译---图像去噪技术研究

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1、中文5200字，2800单词，16500英文字符PDF外文：http:/  SURVEY OF IMAGE DENOISING TECHNIQUES Murkesh C. Motwani  Image Process Technology, Inc. 1776 Back Country RoadReno, NV89521USA (775) 448-7816  mukeshimage- Murkesh C. Gadiya  University of Pune, India Vishwakarma Inst. of Tech. Pune 411337, I

2、NDIA 91-9884371488 mukesh_ RakhiC.MotwaniUniversity of Nevada, Reno Dept of Comp. Sci.&Engr.Reno, NV89557USA (775) 853-7897 Frederick C. Harris, Jr.University of Nevada, Reno  Dept of Comp. Sci. & Engr.,Reno, NV 89557 USA  (775) 784-6571  Abstract Removing noise from the origi

3、nal signal is still a challenging problem for researchers. There have been several published 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

4、approaches are 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. Introduction Digital images play an important role both in daily life applications such as satellite t

5、elevision, magnetic resonance imaging, computer tomography as well as in areas of research and technology such as geographical information systems and astronomy. Data sets collected by image sensors are generally contaminated by noise. Imperfect instruments, problems with the data acquisition proces

6、s, and interfering natural phenomena can all degrade the data of interest. 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 de-no

7、ising 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 different methodologies for noise reduction (or de-noising) giving an insight as to wh

8、ich algorithm should be used to find the most reliable estimate of the original image data given its degraded version. Noise modeling in images is greatly affected by capturing instruments, data transmission media, image quantization and discrete sources of radiation. Different algorithms are used d

9、epending on the noise model Most of the natural images are assumed to have additive random noise which is modeled as a Gaussian. Speckle noise is observed in ultrasound images whereas Ricans noise affects MRI images. The scope of the paper is to focus on noise removal techniques for natural images.

10、2. Evolution of Image De-noising Research Image De-noising has remained a fundamental problem in the field of image processing. Wavelets give a superior performance in image de-noising due to properties such as sparsest and mull-tire solution structure. With Wavelet Transform gaining popularity in t

11、he last two decades various algorithms for de-noising in wavelet domain were introduced. The focus was shifted from the Spatial and Fourier domain to the Wavelet transform domain. Ever since Donohos Wavelet based threshold approach was published in 1995, there was a surge in the de-noising papers be

12、ing published. Although Donohos concept was not revolutionary, his methods did not require tracking or correlation of the wavelet maxima and minima across the different scales as proposed by Mallat. Thus, there was a renewed interest in wavelet based de-noising techniques since Donoho demonstrated a

13、 simple approach to a difficult problem. Researchers published different ways to compute the parameters for the threshold of wavelet coefficients. Data adaptive thresholds were introduced to achieve optimum value of threshold. Later efforts found that substantial improvements in perceptual quality c

14、ould be obtained by translation invariant methods based on threshold of an Un-decimated Wavelet Transform. These threshold techniques were applied to the non-orthogonal wavelet coefficients to reduce artifacts. Multi-wavelets were also used to achieve similar results. Probabilistic models using the

15、statistical properties of the wavelet coefficient seemed to outperform the thresholding techniques and gained ground. Recently, much effort has been devoted to Bayesian de-noising in Wavelet domain. Hidden Markov Models and Gaussian Scale Mixtures have also become popular and more research continues

16、 to be published. Tree Structures ordering the wavelet coefficients based on their magnitude, scale and spatial location have been researched. Data adaptive transforms such as Independent Component Analysis (ICA) have been explored for sparse shrinkage. The trend continues to focus on using differen

17、t statistical models to model the statistical properties of the wavelet coefficients and its neighbors. Future trend will be towards finding more accurate probabilistic models for the distribution of non-orthogonal wavelet coefficients. 3. Classification of De-noising Algorithms As shown in Figure 1

18、, there are two basic approaches to image de-noising, spatial filtering methods and transform domain filtering methods. 3.1 Spatial Filtering A 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.

19、I. Non-Linear Filters With 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 higher region of frequency spectrum. Generally spatial filters remove no

20、ise 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 nonlinear median- type filters such as weighted median, rank conditioned rank selection, and relaxed median have been developed to overcome this drawback.

21、II. Linear Filters A mean filter is the optimal linear filter for Gaussian 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 requi

22、res 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 correspond to choosing the window size. To overcome the weakness of the Wiener filtering,

23、Donoho and Johnstone proposed the wavelet based denoising scheme in. 3.2 Transform Domain Filtering The transform domain filtering methods 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 popular. 3.2.1 Spatial-Frequency Filtering Spatial-frequency filtering refers use of low pass filters using Fast Fourier