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	Image Deno sing via Multiscale Nonlinear Diffusion Models 期刊论文 OAI收割 SIAM JOURNAL ON IMAGING SCIENCES, 2017, 卷号: 10, 期号: 3, 页码: 1234-1257 作者: Feng, Wensen; Qiao, Peng; Xi, Xuanyang; Chen, Yunjin \| 收藏 \| 浏览/下载：38/0 \| 提交时间：2018/03/03 Image Denoising Multiscale Pyramid Image Representation Trainable Nonlinear Reaction Diffusion Model Gaussian Denoising Poisson Denoising
	Seismic data decomposition using sparse Gaussian beams 期刊论文 OAI收割 CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2013, 卷号: 56, 期号: 11, 页码: 3887-3895 作者: Liu Peng; Wang Yan-Fei; Yang Ming-Ming; Yang Chang-Chun; Sholpanbaev, B. B. \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2017/12/22 Gaussian Beam Sparse Decomposition Denoising
	Remote sensing image restoration using estimated point spread function (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Information, Networking and Automation, ICINA 2010, October 17, 2010 - October 19, 2010, Kunming, China 作者: Yang L.; Yang L. 收藏 \| 浏览/下载：38/0 \| 提交时间：2013/03/25 In order to reduce image blur caused by the degradation phenomenon in the imaging process the acquired images of the space remote sensing camera are restored. First the frequency-domain notch filter is adopted to remove strip noises in the images. Then degradation function which is referred to as the point spread function (PSF) of the imaging system is estimated using the knife-edge method. To improve the accuracy of the estimation the estimated PSF is adjusted with Gaussian fitting. Finally the images are restored by Wiener filtering with the fitted PSF. The restoration results of the remote sensing images show that almost all strip noises are eliminated by the notch filter. After denoising and restoration the variance of the remote sensing image worked with in this paper increases 30.979 and the gray mean gradient increases 3.312. Due to Gaussian fitting the accuracy of the PSF estimation is heightened. Image restoration with the final PSF is benefit to interpreting and analyzing the remote sensing images. After restoration the contrasts of the restored images are increased and the visual effects become clearer. 2010 IEEE.
	CR image filter methods research based on wavelet-domain hidden markov models (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang J.-L.; Wang J.-L.; Li D.-Y.; Wang Y.-P. 收藏 \| 浏览/下载：20/0 \| 提交时间：2013/03/25 In the procedure of computed radiography imaging we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail the author think that the major two noises are Gaussian white noise and Poisson noise. Then the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images a multiscale image filtering algorithm which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM according to scale size and achieve the experiments as well as the comparison with other denoising methods were presented at last.
	A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Y.; Li Y.; Li Y.; Li Y. 收藏 \| 浏览/下载：41/0 \| 提交时间：2013/03/25 This paper proposed a new approach for the removal of mixed noise. There are many different ways in image denoising. Donoho et al have proposed a method for image de-noising by thresholding ambiguity is often resulted in determining the correspondence of a modulus maximum to a singularity. In the light and indeed we combine the merits of the two techniques to form a new approach for the removal of mixed noise. At first the application of their method to image denoising has been extremely successful. But the method of Donoho is based on the assumption that the type of noise is only additive Gaussian noise we used wavelet singularity detection (WSD) technique to analyze singularities of signal and noise. According to the characteristic that wavelet transform modulus maxima of impulse noise rapidly decreases as the scale increases in wavelet domain which is not successful for impulse noise. Mallat has also presented a method for signal denoising by discriminating the noise and the signal singularities through an analysis of their wavelet transform modulus maxima (WTMM). Nevertheless it can be accurately located with multiscale space by going through dyadic orthogonal wavelet transform and removed. Furthermore the Gaussian noise is also removed through a level-dependent thresholding algorithm the tracing of WTMM is not just tedious procedure computationally algorithm. The experimental results demonstrate that the proposed method can effectively detect impulse noise and remove almost all of the noise while preserve image details very well.