Image De-noising Algorithm Research Articles

Application of HS-LMBP Hybrid Neural Network Algorithm in Image Denoising

In order to overcome the problems such as poor global search ability, slow convergence rate, and easy to fall into local minimum values in the image denoising process of traditional BP neural networks, the HS-LMBP hybrid neural network image denoising algorithm is proposed which combines the harmony search algorithm and the LMBP algorithm. The HS-LMBP hybrid neural network algorithm combines the high speed of the LMBP algorithm and the global nature of the HS algorithm, which can be a good improvement to the existing problems of the BP algorithm model. Compared with the Wiener filtering, BP, LMBP and PSO-LMBP model image denoising effects, the denoising model using HS-LMBP neural network algorithm has a better denoising effect.

A new algorithm for ECG interference removal from single channel EMG recording.

This paper presents a new method to remove electrocardiogram (ECG) interference from electromyogram (EMG). This interference occurs during the EMG acquisition from trunk muscles. The proposed algorithm employs progressive image denoising (PID) algorithm and ensembles empirical mode decomposition (EEMD) to remove this type of interference. PID is a very recent method that is being used for denoising digital images mixed with white Gaussian noise. It detects white Gaussian noise by deterministic annealing. To the best of our knowledge, PID has never been used before, in the case of EMG and ECG separation or in other 1D signal denoising applications. We have used it according to this fact that amplitude of the EMG signal can be modeled as white Gaussian noise using a filter with time-variant properties. The proposed algorithm has been compared to the other well-known methods such as HPF, EEMD-ICA, Wavelet-ICA and PID. The results show that the proposed algorithm outperforms the others, on the basis of three evaluation criteria used in this paper: Normalized mean square error, Signal to noise ratio and Pearson correlation.

The Two-Dimensional Wavelet Transform De-noising and Combining with Side Scan Sonar Image

This paper puts forward an image de-noising method based on 2D wavelet transform with the application of the method in seabed identification data collection system. Two-dimensional haar wavelets in image processing presents a unified framework for wavelet image compression and combining with side scan sonar image. Seabed identification target have 7 target detection in side scan sonar imagery result. The vibration signals were analyzed to perform fault diagnosis. The obtained signal was time-domain signal. The experiment result shows that the application of 2D wavelet transform image de-noising algorithm can achieve good subjective and objective image quality and help to collect high quality data and analyze the images for the data center with optimum effects, the features from time-domain signal were extracted. 3 vectors were formed which are v1, v2, v3. In Haar wavelet retained energy is 93.8 %, so from the results, it has been concluded that Haar wavelet transform shows the best results in terms of Energy from De-noised Image processing with side scan sonar imagery.

Various Types of Image Noise and De-noising Algorithm

Various Types of Image Noise and De-noising Algorithm

A New Approach for Image De-Noising Algorithm Based on Bayesian Estimation

Based on the problems that traditional image de-noising algorithms easily to lose details features and always has low signal-to-noise ratio, inspired by the cross breeding method and the genetic algorithm, this paper proposes an image hybrid wavelet transform image de-noising algorithm based on Bayesian estimation. The proposed algorithm uses the Bayesian wavelet de-noised image as the male parent, and the Wiener filtering image as the female parent, the picked individual was prepared for the crossover and mutation operations. The optimal offspring will be choosing as the final solution algorithm and the decoding reduction as the de-noised image. The peak signal to noise ratio of the algorithm is much higher than that of the traditional algorithm, and the visual effect is better. The experimental results show that this method can not only eliminate the image noise effectively, but also can preserve the image edges and other details.

Research on the Novel Image De-noising Algorithm Based on Fractal Theory and the Multi-scale Geometric

In this paper, we conduct research on the novel image de-noising algorithm based on fractal theory and the multi-scale geometric. Dimension is a feature of geometry. As is known to all, point is zero dimensional, straight line is one-dimensional, plane is two-dimensional. When measuring the length of the straight line segment, if use to measure per unit area of the square, is zero, the result of the measure that adopted by the scale is too large, Similarly, when measuring circular surface area, if to measure with unit length of the line, to infinity, the result of the measure adopted by the scale is too thin. With this theoretical support, we propose the novel research on the de-noising algorithm that could integrate the advantages of the multi-scale geometric which is meaningful.In this paper, we conduct research on the novel image de-noising algorithm based on fractal theory and the multi-scale geometric. Dimension is a feature of geometry. As is known to all, point is zero dimensional, straight line is one-dimensional, plane is two-dimensional. When measuring the length of the straight line segment, if use to measure per unit area of the square, is zero, the result of the measure that adopted by the scale is too large, Similarly, when measuring circular surface area, if to measure with unit length of the line, to infinity, the result of the measure adopted by the scale is too thin. With this theoretical support, we propose the novel research on the de-noising algorithm that could integrate the advantages of the multi-scale geometric which is meaningful.

An Adaptive Image De-noising Algorithm Based on Edge Preservation

An Adaptive Image De-noising Algorithm Based on Edge Preservation

Textured Enhanced Image De-noising using Fast Wavelet Transform

The digital imaging technologies possess very hasty development that uses Giga-pixels to store an image. Image de-noising algorithms plays a significant role in the restoration process. In an image the texture regions are homogeneous and are composed of local descriptor, a trade off exist between visual quality of image and the enhanced texture regions. In the existing paper the Gradient Histogram Preservation (GHP) method on the enhanced image regions have a limitation where it cannot be directly applied to nonadditive noise removal such as Multiplicative Poisson Noise (MPN) and Signal-Dependent Noise (SDN) to overcome the limitation Fast Wavelet Transform is used. In this work an image is first added with different types of noise like Additive White Gaussian Noise (AWGN), Salt and Pepper Noise, Poisson Noise, Signal Dependent noise and Flicker Noise, the noisy image is restored using filters, next the enhanced texture region of the image is chosen which is blurred or deformed and the fine details of the texture is obtained using Fast Wavelet Transform (FWT). The proposed work is analyzed in Frequency domain by considering various parameters like Peak Signal to Noise Ratio (PSNR), Correlation Factor (CF) and Standard Deviation (SD) and the quality of the enhanced region of the image is improved to the best level than the conventional noise removal algorithms.

Method of Image De-Noising Based on Non-Noisy Atoms Self Adaptive and Sparse Representation

In allusion to the losses of image detail and texture structure information during image de-noising process, an image de-noising algorithm based on non-related dictionary learning is proposed in this paper. Firstly, this algorithm is adopted to obtain selfadaption redundant dictionary for the noisy image through the dictionary learning algorithm; secondly, HOG features and gray-level statistical features of each atom in the dictionary are extracted to form the feature set, and meanwhile the feature set of the atoms is adopted to divide the atoms into two types (non-noisy atoms and noisy atoms); finally, the non-noisy atoms are adopted to recover the image, thus to realize the denosing purpose. The experiment result shows: the proposed algorithm does not need to know the prior information of the noise and PSNR performance thereof is better than that of existing algorithms, and meanwhile the proposed algorithm can well keep the image detail and texture structure information, thus to improve visual effect.

Recursive anisotropic diffusion denoising

A recursive anisotropic diffusion algorithm for image denoising is proposed. Adopting the conventional anisotropic diffusion model, extending it to a recursive model leads to better noise removal with the same parameters, and equivalent or improved results with fewer iterations is showed. The reduction in the number of iterations leads to better edge preservation is also demonstrated. Visual and objective performance evaluations both show superior results compared with the classical model. Furthermore, tests on real medical images show successful noise removal with good edge preservation.

Novel Methods for Microglia Segmentation, Feature Extraction, and Classification.

Segmentation and analysis of histological images provides a valuable tool to gain insight into the biology and function of microglial cells in health and disease. Common image segmentation methods are not suitable for inhomogeneous histology image analysis and accurate classification of microglial activation states has remained a challenge. In this paper, we introduce an automated image analysis framework capable of efficiently segmenting microglial cells from histology images and analyzing their morphology. The framework makes use of variational methods and the fast-split Bregman algorithm for image denoising and segmentation, and of multifractal analysis for feature extraction to classify microglia by their activation states. Experiments show that the proposed framework is accurate and scalable to large datasets and provides a useful tool for the study of microglial biology.

Noise Estimation for Images using Eigen Values and Frobenius norm

Performance of image denoising algorithm is critically dependent on the accuracy of noise level estimation. In the estimation process, restricting the influence of image content on the estimation dataset is a major challenge. In this paper, we propose a novel method which involves the application of Frobenius norm as the basis of content energy measurement. It involves the truncation of SVD (Singular Value Decomposition) components thereby effectively restricting the influence of image details. Application of linear regression for determining content parameter enhances the application scope of the proposed method. The experimental results demonstrates the effectiveness of the proposed approach. Keywords—Frobenius norm, noise estimation, singular value decomposition, linear regression, additive white gaussian noise I. INTRODUCTION Application of Image Denoising or Filtering algorithms is desirable for images corrupted with additive white gaussian noise. This step is applied before many processing algorithms such as image segmentation, restoration etc. Many of these filtering or denoising algorithms rely on the information about the incurred level of noise which is assumed to be known apriori. In real-time applications, this information is obtained by estimation techniques. Accuracy and reliability of estimation algorithms thus affects the filtering or denoising processes concomitantly. More often the noise is assumed to be zero mean additive white gaussian in nature (3), whose distribution is characterized by following equation:

Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm

Medical image denoising is the main step in medical diagnosis, which removes the noise without affecting relevant features of the image. There are many algorithms that can be used to reduce the noise such as: threshold and the sparse representation. The K-SVD is one of the most popular sparse representation algorithms, which is depend on Orthogonal Matching Pursuit (OMP) and Discrete Cosine Transform (DCT) dictionary. In this paper, an algorithm for image denoising was designed to develop K-SVD by using Regularized Orthogonal Matching Pursuit (ROMP) over log Gabor wavelet adaptive dictionary. To evaluate the performance of the proposed techniques, the results were compared with the results of DCT and Gabor wavelet dictionary. The numerical results show that the performance of our algorithm is more efficient in medical image denoising. Keywordsrepresentation (SR), K-SVD, log-Gabor wavelet dictionary, regularized orthogonal matching pursuit and orthogonal matching pursuit.

영상 잡음 제거를 위한 주성분 분석 기반 비 지역적 평균 알고리즘의 효율적인 공분산 행렬 계산 방법

본 논문에서는 영상에 존재하는 잡음 (noise) 들을 제거하는 방법 중 하나인 비 지역적 평균 (non-local means, NLM) 알고리즘을 먼저 소개하고 비 지역적 평균 알고리즘의 개선된 방법 중 하나인 주성분 분석 (principal component analysis, PCA) 기반의 알고리즘에 대해서도 소개한다. 주성분 분석을 활용하기 위해서는 선행적으로 공분산 행렬 (covariance matrix)을 구해야 하는데, 영상의 모든 픽셀들을 대상으로 하였을 때 이 공분산 행렬을 구하기 위해서는 큰 크기를 가지는 행렬 곱 연산이 필요하다. 만약 비 지역적 평균 알고리즘의 영상 패치 (neighborhood patch) 의 크기를 S × S = S², 영상 전체의 픽셀 수를 Q라고 한다면 공분산 행렬을 구하기 위해서는 S² × Q 크기의 행렬 곱 연산이 필요하게 된다. 이는 영상의 특성을 고려하면 비효율적인 연산이다. 따라서 본 논문에서는 공분산 행렬을 효율적으로 구하기 위해, 영상 패치들간의 일정 간격을 유지하면서 샘플링을 하는 방법을 제안하고자 한다. 최종적으로, 샘플링 후에는 S² × floor (Width/l) × (Height/l) 크기를 가진 행렬의 곱 연산으로 공분산 행렬을 구할 수 있다. This paper introduces the non-local means (NLM) algorithm for image denoising, and also introduces an improved algorithm which is based on the principal component analysis (PCA). To do the PCA, a covariance matrix of a given image should be evaluated first. If we let the size of neighborhood patches of the NLM S × S², and let the number of pixels Q, a matrix multiplication of the size S² × Q is required to compute a covariance matrix. According to the characteristic of images, such computation is inefficient. Therefore, this paper proposes an efficient method to compute the covariance matrix by sampling the pixels. After sampling, the covariance matrix can be computed with matrices of the size S² × floor (Width/l) × (Height/l).

Application of Wavelet and Wiener Filtering Algorithm in Image De-Noising

Image is often easily polluted by noise in the process of image processing, so image de-noising is an important step in the field of image processing. Based on the wavelet threshold de-noising algorithm, an improved image de-noising algorithm based on wavelet and Wiener filter is proposed in this paper, which can effectively reduce the Gaussian white noise. Firstly we use wavelet soft threshold to reduce noise, then use Wiener filter to process the image and get the valuation of the image. Experimental results show that the proposed algorithm on image de-noising not only can effectively suppress Gaussian white noise, but also can well retain the details of image edges.

Image Denoising Based on K-means Singular Value Decomposition

The image is usually polluted by noises in its acquisition and transmission and noises are of great importance in the image quality, therefore, image de-noising has become a significant technique in image analysis and processing. In the image de-noising based on sparse representation, one of the hot spots in recent years, the useful image information has certain structural features, which coincide with the atomic structure while noises don’t have such features, therefore, sparse representation can separate the useful information from the noises effectively so as to achieve the purpose of de-noising. In view of the above-mentioned theoretical basis, this paper proposes an image de-noising algorithm of sparse representation based on K-means Singular Value Decomposition (K-SVD). This method can integrate the construction and optimization of over-complete dictionary, train the atom dictionary with the image samples to be decomposed and effectively build the atom dictionary that reflects various image features to enhance the de-noising performance of the algorithm in this paper. Through simulation analysis, this method can conduct noise filtration on the image with different noise densities and its de-noising effect is also better than other methods.

A Directional Bi-dimensional Empirical Mode Decomposition Based Image De-noising Algorithm

A Directional Bi-dimensional Empirical Mode Decomposition Based Image De-noising Algorithm

Image denoising and enhancement based on adaptive fractional calculus of small probability strategy

This paper presents two methods to deal with the problem that traditional image denoising algorithms may easily neglect image texture details. The first one is global adaptive fractional integral algorithm (GAFIA) which deals with common noises. It selects the optimal integral order of each pixel based on the local average gradient. The second is image denoising and enhancement algorithm based on adaptive fractional calculus of small probability strategy (AFC-SPS) which deals with salt & pepper noise. It regards the appearance of noise points as small probability events, divides them, and segments the image edges and weak textures by the improved two-dimensional Otsu algorithm. Then, the function of adaptive fractional order is constructed. Experimental results show that, both of the methods have good image denoising effect, and the AFC-SPS algorithm has a better effect than other methods in enhancing the edge and preserving the texture.

RHFMs similarity based nonlocal means image denoising in PDTDFB domain

It is a challenging work to design an edge-preserving image denoising scheme. Nonlocal means methods have been widely adopted as simple algorithms for image denoising, but they tend to overblur the image and sharpen the boundary with many texture details lost. In this paper, we propose a new nonlocal means image denoising approach in shiftable complex directional pyramid (PDTDFB) domain, in which the robust radial harmonic Fourier moments (RHFMs) are utilized. The novelty of the proposed approach includes: (1) PDTDFB based multi-scale structural similarity is explored, which has good robustness to noise interference, (2) Geometric invariant radial harmonic Fourier moments (RHFMs) are introduced into similarity measure function, which can improve the denoising ability. Experimental results demonstrate that our image denoising method can obtain better performances in terms of both subjective and objective evaluations than some other state-of-the-art denoising techniques. Especially, it can preserve edges very well while removing noise.

Robust Estimators in Fuzzy Logic Cellular-Automata Salt and Pepper Noise Filtering

In order to increase the performance of computational algorithms in terms of efficiency of estimators, we tested new nonparametric estimators in fuzzy and cellular automata models. In particular, image de-noising algorithms exist to restore digital images corrupted by impulse noise. These algorithms may do poorly in many common cases, for example, when high contrast and sharp edges lead to outliers, spikes, or non-symmetric patterns for neighborhing pixels. This would stem from the choices of estimators in the algorithms. We investigated new nonparametric estimators and compared to existing methods in simulation study. We detected better performances of our new methods under various situations.