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image denoising algorithm via best wavelet packet base using Wiener cost function

IET image PROCESSING 2007年第3期1卷 311-318页

作者： Shui, P.-L. Zhou, Z.-F. Li, J.-X. Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

A new cost function, namely, the Wiener cost function, is introduced to find the best wavelet packet (WP) base in image denoising. Unlike the existing entropy-type cost functions in image compression, the Wiener cost function depends on both sparseness of image representation and noise level. Combining the Wiener cost function and the doubly local Wiener filtering scheme, a new image denoising algorithm is proposed using the best wavelet packet bases. Owing to unknown true image in denoising, a pilot image with less noise is required to find the best wavelet packet base, which is obtained by the existing denoising algorithms. From the pilot image, the best 2D wavelet packet tree is searched in terms of the Wiener cost function and the energy distributions of the image in the best wavelet packet domain are also estimated. Further, the image is recovered by applying the local Wiener filtering to the best wavelet packet coefficients of the noisy image. The experimental results show that for images of structural textures, for example 'Barbara' and texture images, the proposed algorithm greatly improves denoising performance as compared with the existing state-of-the-art algorithms.

关键词： image compression Noise reduction Noisy image image denoising Noise levels Wiener filters Wavelet packets bockwurst GRAIN PATTERN image representation Cost functions images Data compression image denoising algorithm energy distribution

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image denoising algorithm Based on Nonlocal Regularization Sparse Representation

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IEEE SENSORS JOURNAL 2020年第20期20卷 11943-11950页

作者： Du, Hongchun Jiangsu Food & Pharmaceut Sci Coll Sch Basic Educ Huaian 223000 Peoples R China

image denoising is essential for subsequent image processing and application, and it ensures that people can obtain valid information of images more accurately. The key to image denoising is how to preserve the structure and detail information in the original image while removing noise. By the further analysis of image features, based on K-SVD image denoising algorithm, an image sparse regularization denoising algorithm based on structural similarity is proposed. In this paper, the structural similarity is used instead of the mean square error as the fidelity term in the sparse regularization denoising model. By using it as an optimization criterion, the geometric features of the image can be restored to the utmost. The experimental results show that compared with the K-SVD image denoising algorithm, the algorithm can better preserve the structural information of the image and obtain better image visual effects while effectively denoising.

关键词： Dictionaries Noise reduction image denoising Machine learning Optimization Adaptation models Sensors image denoising algorithm sparse regularization non-local self-similarity structural similarity

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image denoising algorithm based on contourlet transform for optical coherence tomography heart tube image

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IET image PROCESSING 2013年第5期7卷 442-450页

作者： Guo, Qing Dong, Fangmin Sun, Shuifa Lei, Bangjun Gao, Bruce Z. China Three Gorges Univ Inst Intelligent Vis & Image Informat Yichang 443002 Hubei Peoples R China Clemson Univ Dept Bioengn Clemson SC 29634 USA

Optical coherence tomography (OCT) is becoming an increasingly important imaging technology in the Biomedical field. However, the application of OCT is limited by the ubiquitous noise. In this study, the noise of OCT heart tube image is first verified as being multiplicative based on the local statistics (i.e. the linear relationship between the mean and the standard deviation of certain flat area). The variance of the noise is evaluated in log-domain. Based on these, a joint probability density function is constructed to take the inter-direction dependency in the contourlet domain from the logarithmic transformed image into account. Then, a bivariate shrinkage function is derived to denoise the image by the maximum a posteriori estimation. Systemic comparative experiments are made to synthesis images, OCT heart tube images and other OCT tissue images by subjective assessment and objective metrics. The experiment results are analysed based on the denoising results and the predominance degree of the proposed algorithm with respect to the wavelet-based algorithm. The results show that the proposed algorithm improves the signal-to-noise ratio, whereas preserving the edges and has more advantages on the images containing multi-direction information like OCT heart tube image.

关键词： cardiology image denoising maximum likelihood estimation medical image processing optical tomography probability wavelet transforms image denoising algorithm contourlet transform optical coherence tomography heart tube image OCT heart tube image imaging technology biomedical field local statistics noise variance joint probability density function logarithmic image transformation bivariate shrinkage function maximum a posteriori estimation OCT tissue images subjective assessment objective metrics wavelet-based algorithm signal-to-noise ratio improvement

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A New image denoising algorithm Based on Massive image Database 5

A New Image Denoising Algorithm Based on Massive Image Datab...

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5th International Conference on Information Science and Technology (ICIST)

作者： Wang, Haoqian Yang, Jiangfeng Wang, Xingzheng Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781479974894

image denoising is the basic problem in image processing and computer vision. Pictures are always contaminated with Gaussian noise in the capture and transmission process. The adaptive dictionary learning algorithms can remove Gaussian noise very well, but it takes a lot of time in training dictionary so that these methods cannot be applied in the actual scene. So we would like to solve the problem using massive image database. Firstly in the offline stage, hash coefficients are calculated and a dictionary is trained for every image in the database. Second, in the online stage, several reference images are searched by comparing the hash coefficients. For each given noisy block the best dictionary is selected according to the best sparse operator. Finally, all blocks are recovered using these selected dictionaries thus the denoised image is obtained by weighted averaging. Experiments show that the proposed method can remove Gaussian noise very well and preserve the details and the computation complexity is significantly reduced compared with other dictionary learning algorithms.

关键词： Dictionaries as Topic visual databases Gaussian noise image denoising algorithm image denoising images learning algorithms Noise hashing

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A unified iterative denoising algorithm based on natural image statistical models: derivation and examples

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OPTICS EXPRESS 2008年第2期16卷 975-992页

作者： Tan, Shan Jiao, Licheng Xidian Univ Inst Intelligent Informat Proc Xian Peoples R China

Lots of prior models for natural image wavelet coefficients have been proposed in the last two decades. Although most of them belong to the Scale Mixture of Gaussian (GSM) models, they are of obviously different analytical forms. As a result, Bayesian image denoising algorithms based on these prior models are also very different from each other. In this paper, we develop a novel image denoising algorithm by combining the Expectation Maximization (EM) scheme and the properties of the GSM models. The developed algorithm is of a simple iterative form and can converge quickly. It only uses the derivative information of a probability density function and is suitable for all GSM-type prior models that have an analytical probability density function. The developed algorithm can be viewed as a unified Bayesian image denoising framework. As examples, several classical and recently-proposed prior models for natural image wavelet coefficients are tested and some new results are obtained. (c) 2008 Optical Society of America.

关键词： .GSM belong Obviously algorithm based statistical models PRIOR MODELS natural image wavelet image denoising algorithm EM scheme developed algorithm

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Immersive VR experience based on virtual robots in decorative material environment design simulation

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Zheng, Dongdong Pingdingshan Univ Art & Design Coll Pingdingshan 467000 Henan Peoples R China

Virtual robot and immersive VR experiences provide consumers with new ways of perception in the design process of decorative material environments. This experiential process is virtual, intuitive, and entertaining, and is widely recognized by *** purpose of this study is to improve the processing effect of virtual images of decorative materials by image denoising algorithm. In this paper, the image acquisition and modeling technology based on sensor network and image denoising algorithm are used to process the acquired image data, and the processed image is applied to the virtual system of decorative materials. Through the application of image denoising algorithm, the noise in the image is reduced, and the clarity and authenticity of the virtual image of decorative materials are improved. This makes it possible to select the best decoration scheme more accurately when comprehensive calculation is carried out from various aspects such as aesthetic and functional data, and provides a new feasibility and method for environmental design and decoration material selection. The research results demonstrate that the system can achieve virtual image processing in environmental decoration scene design, thereby improving the level of indoor environmental art design.

关键词： Virtual robot image denoising algorithm Environmental design Virtual image Processing

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image denoising using patch ordering and 3D transformation of patches

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IET image PROCESSING 2019年第13期13卷 2636-2646页

作者： Colak, Ozden Eksioglu, Ender M. TUBITAK BILGEM Informat Technol Inst Kocaeli Turkey Istanbul Tech Univ Elect & Commun Engn Dept Istanbul Turkey

A novel image denoising algorithm which is based on the ordering of noisy image patches into a 3D array and the application of 3D transformations on this image dependent patch cube is proposed. For a given noisy image, the authors extract all the patches with overlaps. Then, they order these patches according to a predefined similarity measure. After reordering, a possibly separable 3D transformation is applied to the reordered 3D patch cube. The transform domain coefficients are thresholded using a suitably calculated thresholding parameter. Afterwards, the proper 3D inverse transformation is applied to these coefficients. The final denoised image is generated by repositioning the processed patches to their original locations on the image canvas. The developed algorithm presents a novel and efficient combination of patch ordering and 3D transformations. The forward analysis transform as defined by this complex procedure can get restated as the application of a single tight frame. This tight frame depends on the noisy image under consideration. This novel, image dependent forward operator which employs 3D transforms results in improved denoising performance. The experimental results indicate that the proposed algorithm achieves state-of-the-art denoising results with complexity comparable to competing methods.

关键词： image denoising reordered 3D patch cube final denoised image processed patches image canvas patch ordering image dependent forward operator 3D transforms results improved denoising performance image denoising algorithm noisy image patches image dependent patch cube predefined similarity measure thresholding parameter separable 3D transformation 3D inverse transformation

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Multi-scale patches based image denoising using weighted nuclear norm minimisation

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IET image PROCESSING 2020年第13期14卷 3161-3168页

作者： Fu, Yuli Xu, Junwei Xiang, Youjun Chen, Zhen Zhu, Tao Cai, Lei He, Weihong South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China

As a prior knowledge, non-local self-similarity (NSS) has been widely utilised in ill-posed problems. Actually, similar textures appear not only in a single scale, but also in different scales. Unlike most existing patch-based methods that only explore NSS in the same scale, a multi-scale patches based image denoising algorithm is proposed in this study. The authors have designed a multi-scale strategy to expand the search space of block-matching, which will increase the probability of finding more similar patches. After that, the weighted nuclear norm minimisation (WNNM) algorithm is employed to reveal latent clean patches. With the join of the multi-scale framework, the performance of WNNM can be improved. The proposed algorithm can be used to solve NSS-based image restoration tasks. In this study, mainly image denoising is studied, and its effectiveness is derived through experiments on widely used test images.

关键词： image restoration image denoising minimisation image texture weighted nuclear norm minimisation algorithm latent clean patches multiscale framework NSS-based image restoration tasks widely used test images multiscale patches nonlocal self-similarity ill-posed problems similar textures single scale existing patch-based methods image denoising algorithm multiscale strategy

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Wavelet based image denoising: VQ-Bayesian technique

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ELECTRONICS LETTERS 1999年第19期35卷 1625-1626页

作者： John, M Sundaresan, SN Ramakrishna, PV Anna Univ Madras Inst Technol Dept Elect Engn Chennai 600044 India

An image denoising algorithm for the wavelet-transform domain of an image is presented. In the proposed method, a Bayesian estimation procedure is used to restore the higher scale wavelet coefficients, while vector quantisation is used to realise a spatially adaptive Bayesian estimation procedure for the lower scales. The proposed algorithm provides an improved noise suppression performance as compared to that of the denoising algorithm of Mallat and Hwang (1992) and also that of Malfait and Roose (1997).

关键词： Bayesian estimation procedure noise image denoising algorithm spatially adaptive Bayesian estimation procedure Integral transforms in numerical analysis wavelet based image denoising Signal processing theory higher scale wavelet coefficients wavelet-transform domain VQ-Bayesian technique interference suppression noise suppression performance Bayes methods Computer vision and image processing techniques Other topics in statistics wavelet transforms Optical, image and video signal processing Integral transforms vector quantisation image enhancement

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COMPRESSIVE SENSING-BASED image denoising USING ADAPTIVE MULTIPLE SAMPLING AND OPTIMAL ERROR TOLERANCE

COMPRESSIVE SENSING-BASED IMAGE DENOISING USING ADAPTIVE MUL...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Wonseok Kang Eunsung Lee Eunjung Chea Aggelos K. Katsaggelos Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Seoul Chung-Ang University Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL 60208 USA

ISBN: (纸本)9781479903573

In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method.

关键词： Tolerances physiologic tolerance Sampling system recovery image denoising image representation images Signal analysis Statistical analysis Statistical methods image denoising algorithm

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