中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共5条，第1-5条

帮助

条数/页：排序方式：
	Design and image restoration research of a cubic-phase-plate system (EI CONFERENCE) 会议论文 OAI收割 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, April 26, 2010 - April 29, 2010, Dalian, China 作者: Zhang J.; Zhang J.; Zhang J.; Shi G.; Zhang X. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 Wave-front coding technology is a novel jointly optical and digital imaging technology which can greatly extend the depth of focus of optical systems. The image restoration process is an important part of wave-front coding technology. Using wave-front coding makes the modulation transfer function(MTF) values of the optical systems change little over a range of several times the depth of focus which means the system MTF is quite insensitive to defocus and there is no zero in the passband. So we can design a single filter for the restoration of images in different defocus positions. However it's hard to avoid noise during image acquisition and transmission processes. These noises will be amplified in the image restoration especially in the high frequency part when the MTF drop is relatively low. The restoration process significantly reduces the system signal to noise ratio this way. Aimed at the problem of noise amplification a new algorithm was proposed which incorporated wavelet denoising into the iterative steps of Lucy-Richardson algorithm. Better restoration results were obtained through the new algorithm effectively solving the noise amplification problem of original LR algorithm. Two sets of identical triplet imaging systems were designed in one of which the cubic-phase-plate was added. Imaging experiments of the manufactured systems were carried and the images of a traditional system and a wave-front coded system before and after decoding were compared. The results show that the designed wave-front coded system can extend the depth of focus by 40 times compared with the traditional system while maintaining the light flux and the image plane resolution. 2010 Copyright SPIE - The International Society for Optical Engineering.
	A Lattice Boltzmann Method for Image Denoising 期刊论文 OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2009, 卷号: 18, 期号: 12, 页码: 2797-2802 作者: Chang, Qianshun; Yang, Tong \| 收藏 \| 浏览/下载：23/0 \| 提交时间：2018/07/30 Algorithm of denoising fast algorithm image restoration lattice Boltzmann method parallel algorithm
	A Compound Algorithm of Denoising Using Second-Order and Fourth-Order Partial Differential Equations 期刊论文 OAI收割 NUMERICAL MATHEMATICS-THEORY METHODS AND APPLICATIONS, 2009, 卷号: 2, 期号: 4, 页码: 353-376 作者: Chang, Qianshun; Tai, Xuecheng; Xing, Lily \| 收藏 \| 浏览/下载：20/0 \| 提交时间：2018/07/30 Algorithm of denoising image restoration total variation second-order functional
	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：18/0 \| 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：38/0 \| 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.