外文翻譯---圖像去噪技術(shù)研究（英文）

1、Survey of Image Denoising Techniques Mukesh C. Motwani Mukesh C. Gadiya Rakhi C. Motwani Image Process Technology, Inc. University of Pune, India University of Nevada, Reno 1776 Back Country Road

2、 Vishwakarma Inst. of Tech. Dept of Comp. Sci. & Engr., Reno, NV 89521 USA Pune 411337, INDIA Reno, NV 89557 USA (775) 448-7816 91-9884371488 (775) 853-7897 mukesh@image-proce

3、ss.com mukesh_gadiya@satyam.com rakhi@cs.unr.edu Frederick C. Harris, Jr. University of Nevada, Reno Dept of Comp. Sci. & Engr., Reno, NV 89557 USA (775) 784-6571 fredh@cs.unr.edu Abstract Rem

4、oving noise from the original 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 present

5、s a review of some significant work in the area of image denoising. After a brief introduction, some popular approaches are classified into different groups and an overview of various algorithms and analysis is provi

6、ded. Insights and potential future trends in the area of denoising are also discussed. 1. Introduction Digital images play an important role both in daily life applications such as satellite television, magnetic reson

7、ance 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 nois

8、e. Imperfect instruments, problems with the data acquisition process, and interfering natural phenomena can all degrade the data of interest. Furthermore, noise can be introduced by transmission errors and compress

9、ion. Thus, denoising is often a necessary and the first step to be taken before the images data is analyzed. It is necessary to apply an efficient denoising technique to compensate for such data corruption. Image de

10、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 denoising) givi

11、ng an insight as to which 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, dat

12、a transmission media, image quantization and discrete sources of radiation. Different algorithms are used depending on the noise model. Most of the natural images are assumed to have additive random noise which is mo

13、deled as a Gaussian. Speckle noise [1] is observed in ultrasound images whereas Rician noise [2] affects MRI images. The scope of the paper is to focus on noise removal techniques for natural images. 2. Evolution of

14、 Image Denoising Research Image Denoising has remained a fundamental problem in the field of image processing. Wavelets give a superior performance in image denoising due to properties such as sparsity and multiresolu

15、tion structure. With Wavelet Transform gaining popularity in the last two decades various algorithms for denoising in wavelet domain were introduced. The focus was shifted from the Spatial and Fourier domain to the

16、Wavelet transform domain. Ever since Donoho’s Wavelet based thresholding approach was published in 1995, there was a surge in the denoising papers being published. Although Donoho’s concept was not revolutionary, his

17、 methods did not require tracking or correlation of the wavelet maxima and minima across the different scales as proposed by Mallat [3]. Thus, there was a renewed interest in wavelet based denoising techniques since