scholarly journals EDGE–PRESERVING DENOISING BASED ON DYNAMIC PROGRAMMING ON THE FULL SET OF ADJACENCY GRAPHS

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 55-60
Author(s):  
P. C. Thang ◽  
A. V. Kopylov ◽  
S. D. Dvoenko
Keyword(s):  
Dynamic Programming ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Diagnosis ◽  
Additive White Gaussian Noise ◽  
Experimental Results ◽  
Crucial Importance ◽  
High Quality ◽  
Edge Preserving ◽  
Adjacency Graphs

The ability of a denoising procedure to preserve fine image structures when suppressing unwanted noise has crucial importance for an accurate and effective medical diagnosis. We introduce here a new procedure of edge-preserving denoising for medical images, that combines the flexibility in prior assumptions, and computational effectiveness of parametric multi-quadratic dynamic programming with the increased accuracy of a tree-like representation of a discrete lattice based on the full set of possible adjacency graphs of image elements. Proposed procedure can effectively remove an additive white Gaussian noise with high quality. We provide experimental results in image denoising as well as comparison with related methods.

Image denoising via Bayesian estimation of local variance with Maxwell density prior

2015 ◽  
Vol 06 (02) ◽  
pp. 1550002
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Prior Distribution ◽  
Additive White Gaussian Noise ◽  
Maximum A Posteriori ◽  
Map Estimation ◽  
A Posteriori ◽  
Bayesian Techniques ◽  
Local Variance ◽  
Selection Of

The need for efficient image denoising methods has grown with the massive production of digital images and movies of all kinds. The distortion of images by additive white Gaussian noise (AWGN) is common during its processing and transmission. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. Indeed, one of the cruxes of the Bayesian image denoising algorithms is to estimate the local variance of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with Maxwell density prior for local observed variance and Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by analytical and computational tractability. The experimental results show that the proposed method yields good denoising results.

scholarly journals Modified Perona-Malik Equation and Computer Simulation for Image Denoising

2014 ◽  
Vol 8 (1) ◽  
pp. 37-41
Author(s):  
Zheng Jian Feng ◽  
Huang Chengwei ◽  
Zhang Ji
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Computer Simulation ◽  
Image Denoising ◽  
Digital Image ◽  
Gaussian Noise ◽  
Anisotropic Diffusion ◽  
Difficult Problem ◽  
Experimental Results ◽  
Proposed Model

The edges and textures of a digital image may be destroyed by traditional denoising methods, which is a difficult problem in image denoising. In this paper, anisotropic diffusion algorithm based on Partial differential equation is studied. First, image denoising algorithms based on Perona-Malik model are studied. Second, a modified Perona-Malik model is proposed. In the proposed model, the gradient statistic and edge thresholds are embedded into the Perona-Malik equation. Finally, the effects of this model and some other models are compared and analyzed. The experimental results show that the proposed modified Perona-Malik model outperforms the original Perona-Malik model in removing Gaussian noise, and the edges and textures of the image are well preserved.

scholarly journals Edge-guided second-order total generalized variation for Gaussian noise removal from depth map

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shuaihao Li ◽  
Bin Zhang ◽  
Xinfeng Yang ◽  
Weiping Zhu
Keyword(s):  
Gaussian Noise ◽  
Depth Map ◽  
Noise Removal ◽  
Second Order ◽  
Variational Model ◽  
High Quality ◽  
Edge Preserving ◽  
Total Generalized Variation ◽  
Primal Dual ◽  
Generalized Variation

Abstract Total generalized variation models have recently demonstrated high-quality denoising capacity for single image. In this paper, we present an accurate denoising method for depth map. Our method uses a weighted second-order total generalized variational model for Gaussian noise removal. By fusing an edge indicator function into the regularization term of the second-order total generalized variational model to guide the diffusion of gradients, our method aims to use the first or second derivative to enhance the intensity of the diffusion tensor. We use the first-order primal–dual algorithm to minimize the proposed energy function and achieve high-quality denoising and edge preserving result for depth maps with high -intensity noise. Extensive quantitative and qualitative evaluations in comparison to bench-mark datasets show that the proposed method provides significant higher accuracy and visual improvements than many state-of-the-art denoising algorithms.

A Non-Local Image Denoising Technique Using Adaptive Filter Parameter

2014 ◽  
Vol 556-562 ◽  
pp. 4839-4842
Author(s):  
Song Yuan Tang
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Wiener Filter ◽  
White Gaussian Noise ◽  
Filter Parameter ◽  
Iterative Computation ◽  
Local Mean ◽  
Non Local

This paper proposes a method to obtain the optimal filter parameter of the non-local mean (NLM) algorithm. The parameter is assumed to be a function of the variance of the additive white Gaussian noise and is adaptive estimated. The initialization of the variance of the additive white Gaussian noise is estimated by Wiener filter. Then the NLM filter is used to adaptively estimate the noise variance. The image denoising is an iterative computation till the parameter convergence. Experiments show that the proposed method can improve the quality of the denoised images efficiently.

scholarly journals Iterative based image and video denoising by fractional block matching and transform domain filtering

2021 ◽  
Author(s):  
Amir Mehdizadeh Hemat Abadi ◽  
Mohammad Reza Hosseiny Fatemi
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Weighted Average ◽  
Block Matching ◽  
Objective Measures ◽  
Transform Domain ◽  
Video Denoising ◽  
Simulation Results ◽  
Fractional Motion Estimation

This paper presents an iterative algorithm for image and video denoising which is based on fractional block-matching and transform domain filtering. We propose fractional motion estimation technique to find the most accurate similar blocks for each block of an image which improves sparsity enabling effective image denoising. By taking the advantage of blocks similarity and wavelet transform domain filtering along with weighted average function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a better exploiting of blocks similarity redundancies of noisy images that increase the chance of preserving details and edges in the restored image. Since our algorithm is iterative, we can tradeoff between image denoising degree and computational complexity. In addition, we develop a video denoising algorithm based on the proposed image denoising algorithm. The simulation results of images and videos contaminated by additive white Gaussian noise demonstrate that our algorithm substantially achieves better denoising performance compared with previously published algorithms in terms of subjective and objective measures.

scholarly journals Edge-Preserving Median Filter and Weighted Coding with Sparse Nonlocal Regularization for Low-Dose CT Image Denoising Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Low Dose ◽  
Median Filter ◽  
Similarity Index ◽  
Ct Image ◽  
Edge Preserving ◽  
Low Dose Ct ◽  
Nonlocal Regularization

The impulse noise in CT image was removed based on edge-preserving median filter algorithm. The sparse nonlocal regularization algorithm weighted coding was used to remove the impulse noise and Gaussian noise in the mixed noise, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were calculated to evaluate the quality of the denoised CT image. It was found that in nine different proportions of Gaussian noise and salt-and-pepper noise in Shepp-Logan image and CT image processing, the PSNR and SSIM values of the proposed denoising algorithm based on edge-preserving median filter (EP median filter) and weighted encoding with sparse nonlocal regularization (WESNR) were significantly higher than those of using EP median filter and WESNR alone. It was shown that the weighted coding algorithm based on edge-preserving median filtering and sparse nonlocal regularization had potential application value in low-dose CT image denoising.

scholarly journals Median Filter Aided CNN Based Image Denoising: An Ensemble Aprroach

Algorithms ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 109
Author(s):  
Subhrajit Dey ◽  
Rajdeep Bhattacharya ◽  
Friedhelm Schwenker ◽  
Ram Sarkar
Keyword(s):  
Neural Networks ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Median Filter ◽  
Research Problem ◽  
The Third ◽  
Challenging Research ◽  
Deep Cnn ◽  
Dilation Rate

Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For this purpose, we propose an ensemble learning model that uses the output of three image denoising models, namely ADNet, DnCNN, and IRCNN, in the ratio of 2:3:6, respectively. The first model (ADNet) consists of Convolutional Neural Networks with attention along with median filter layers after every convolutional layer and a dilation rate of 8. In the case of the second model, it is a feed forward denoising CNN or DnCNN with median filter layers after half of the convolutional layers. For the third model, which is Deep CNN Denoiser Prior or IRCNN, the model contains dilated convolutional layers and median filter layers up to the dilated convolutional layers with a dilation rate of 6. By quantitative analysis, we note that our model performs significantly well when tested on the BSD500 and Set12 datasets.

scholarly journals A Residual-Based Kernel Regression Method for Image Denoising

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jiefei Wang ◽  
Yupeng Chen ◽  
Tao Li ◽  
Jian Lu ◽  
Lixin Shen
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
State Of The Art ◽  
Kernel Regression ◽  
Bilateral Filter ◽  
Regression Method ◽  
Experimental Results ◽  
Adaptive Kernel

We propose a residual-based method for denoising images corrupted by Gaussian noise. In the method, by combining bilateral filter and structure adaptive kernel filter together with the use of the image residuals, the noise is suppressed efficiently while the fine features, such as edges, of the images are well preserved. Our experimental results show that, in comparison with several traditional filters and state-of-the-art denoising methods, the proposed method can improve the quality of the restored images significantly.

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