Approach For Noise Removal Research Articles

Hybrid Approach for Noise Removal and Image Enhancement of Brain Tumors in Magnetic Resonance Images

In medical image processing, Magnetic Resonance Imaging (MRI) is one of significant diagnostic techniques. It provides high quality of important information about the analysis of human soft tissue when measured with CT imaging modalities; hence it is suitable for diagnosis at best. However, if it gives quality of information, image may distorted by noise because of image acquisition device and transmission. The noises in MR image reduces the quality of image and also damages the segmentation task which can lead to faulty diagnosis. Noises have to reduce at the same time there is no information loss. This paper propose a hybrid approach to enhance the brain tumor MRI images using combined features of Anisotropic Diffusion Filter (ADF) with Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF). ADF scheme provides a superior performance by removing noise while preserving image details and enhancing edges. MDBUTMF helps in image denoising as well as preserving edges satisfactorily when the noise level is high. The performance of this filter is evaluated by carrying out a qualitative comparison of this method with other filters namely, ADF filter, Modified Decision Algorithm, Median filter, MDBUTMF.

De-noising for NMR oil well logging signals based on empirical mode decomposition and independent component analysis

Inversions of T2-distribution can be severely disturbed by the noise in nuclear magnetic resonance (NMR) oil well logging. Methods to isolate and remove these disturbances are typically based on time-series editing. An alternative approach for noise removal is proposed based on a combination of empirical mode decomposition (EMD) and independent component analysis (ICA), called the EMD-ICA method. Firstly, the NMR oil well logging signals is decomposed into a series of IMFs (intrinsic mode function) with EMD. Then, the successive 3 orders IMF components are combined into a sequence sequentially, and ICA is applied for this sequence. Finally, the obtained results of ICA are used to reconstruct the de-noised signal. Principle and steps of method are presented, then, some simulated signal and measured logging data are processed. The de-noised results are compared with that from Wavelet method and EMD space-time filtering method. The results illustrate that free of noise data sections are preserved because logging data is analyzed through hierarchies, or scale levels, allowing separation of noise from signals with EMD-ICA method. After filtering stage, the two peak value points of T2 curve are highlighted and T2-distribution becomes more reliable comparing with before de-noising. The proposed method reduces the bias error of the estimated parameter and improves the quality of logging data significantly, as well as provides a good basis for further studies of the reservoir.

Denoising and error correction in noisy AES-encrypted images using statistical measures

Cryptography based techniques are used to secure confidential data from unauthorized access. These techniques are very good for the security and protection of the data but are very sensitive to noise. A single bit change in encrypted data can have a catastrophic impact on the decrypted data. This paper addresses the problem of removing bit errors in visual data which are encrypted using the AES algorithm in CBC mode (Cipher Block Chaining). We propose a noise removal approach based on the statistical analysis of each block during the decryption process. Three statistical measures are proposed, i.e. the global variance method (GVM), the mean local variance method (MLVM) and the sum of the squared derivative method (SSDM) for error correction. The proposed approach uses local statistics of the visual data and confusion/diffusion properties of the encryption algorithm to remove errors. Experimental results show that the proposed approach gives better results in removing noise and can be used for noise removal in visual data in the encrypted domain.

SAR Image Despeckling by Sparse Reconstruction Based on Shearlets

Synthetic aperture radar (SAR) image is usually polluted by multiplicative speckle noise, which can affect further processing of SAR image. This paper presents a new approach for multiplicative noise removal in SAR images based on sparse coding by shearlets filtering. First, a SAR despeckling model is built by the theory of compressed sensing (CS). Secondly, obtain shearlets coefficient through shearlet transform, each scale coefficient is represented as a unit. For each unit, sparse coefficient is iteratively estimated by using Bayesian estimation based on shearlets domain. The represented units are finally collaboratively aggregated to construct the despeckling image. Our results in SAR image despeckling show the good performance of this algorithm, and prove that the algorithm proposed is robustness to noise, which is not only good for reducing speckle, but also has an advantage in holding information of the edge.

On-orbit calibration approach for star cameras based on the iteration method with variable weights.

To perform efficient on-orbit calibration for star cameras, we developed an attitude-independent calibration approach for global optimization and noise removal by least-square estimation using multiple star images, with which the optimal principal point, focal length, and the high-order focal plane distortion can be obtained in one step in full consideration of the interaction among star camera parameters. To avoid the problem when stars could be misidentified in star images, an iteration method with variable weights is introduced to eliminate the influence of misidentified star pairs. The approach can increase the precision of least-square estimation and use fewer star images. The proposed approach has been well verified to be precise and robust in three experiments.

Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming

Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways.

Sparse nonlocal priors based two-phase approach for mixed noise removal

Mixed noise removal is a challenging problem due to the complexity of statistical model of image noise. Additive white Gaussian noise (AWGN) combined with impulse noise (IN) is a representative among commonly encountered mixed noise. At present, nonlocal self-similarity (NSS) prior coupled with adaptive regularization have shown great potential in AWGN removal and led to satisfactory denoising performance. However, few studies unify these properties to remove mixture of AWGN and IN. In this paper, we propose a simple yet effective method, namely sparse nonlocal priors based two-phase approach (SNTP), for mixed noise removal. In SNTP, a median-type filter is used to detect outlier pixels which are likely to be corrupted by IN, and the remaining pixels are mainly corrupted by AWGN. We recover the image by encoding free-outlier pixels over a pre-learned dictionary to remove AWGN, and integrate the image sparse nonlocal priors as a regularization term. Meanwhile, adaptive regularization is used to further improve the denoising performance. Experimental results show that the proposed SNTP algorithm outperforms state-of-the-art mixed noise removal methods in terms of both quantitative measures and visual perception quality.

Multimedia Summarization for Social Events in Microblog Stream

Microblogging services have revolutionized the way people exchange information. Confronted with the ever-increasing numbers of social events and the corresponding microblogs with multimedia contents, it is desirable to provide visualized summaries to help users to quickly grasp the essence of these social events for better understanding. While existing approaches mostly focus only on text-based summary, microblog summarization with multiple media types (e.g., text, image, and video) is scarcely explored. In this paper, we propose a multimedia social event summarization framework to automatically generate visualized summaries from the microblog stream of multiple media types. Specifically, the proposed framework comprises three stages, as follows. 1) A noise removal approach is first devised to eliminate potentially noisy images. An effective spectral filtering model is exploited to estimate the probability that an image is relevant to a given event. 2) A novel cross-media probabilistic model, termed Cross-Media-LDA (CMLDA), is proposed to jointly discover subevents from microblogs of multiple media types. The intrinsic correlations among these different media types are well explored and exploited for reinforcing the cross-media subevent discovery process. 3) Finally, based on the cross-media knowledge of all the discovered subevents, a multimedia microblog summary generation process is designed to jointly identify both representative textual and visual samples, which are further aggregated to form a holistic visualized summary. We conduct extensive experiments on two real-world microblog datasets to demonstrate the superiority of the proposed framework as compared to the state-of-the-art approaches.

An Efficient Approach for Removal of Impulse Noise from Highly Corrupted Images

In this paper, a method based on adaptive fuzzy filter is used to denoise image corrupted by impulse noise. The algorithm is proposed for restoration of grayscale as well as color images which are highly corrupted by salt and pepper noise. The algorithm detects level of corruption for each pixel using triangular fuzzy membership function to ensure the replacement of noisy pixels based on degree of corruption. Besides providing ease of computation here we use fully adaptive and automatically adjustable threshold value. Final result shows better performance in the impulse noise reduction over standard images relative to the conventional median filtering approach in terms of both noise reduction and fine detail preservation.

A combined first-order and second-order variation approach for multiplicative noise removal

A combined first-order and second-order variation approach for multiplicative noise removal

Image Denoising Using Spatial Gradient Based Bilateral Filter and Minimum Mean Square Error Filtering

In this paper we propose noise removal approach using spatial gradient based bilateral filter and minimum mean square error filtering. The proposed method consist two-steps process. In first step, we generate a reference image from the given noisy image, by extracting (2W + 1) × (2W + 1) size patches from it and then apply proposed spatial gradient based bilateral filter on each of these patches. To reduce the mean square error, in second step we apply minimum mean square error (MSE) filter on the reference image. Generally all the noise removal approaches change the natural appearance of the restored image, while the proposed method restores the image without affecting its natural appearance.

Subspace-Based Technique for Speckle Noise Reduction in SAR Images

Image-subspace-based approach for speckle noise removal from synthetic aperture radar (SAR) images is proposed. The underlying principle is to apply homomorphic framework in order to convert multiplicative speckle noise into additive and then to decompose the vector space of the noisy image into signal and noise subspaces. Enhancement is performed by nulling the noise subspace and estimating the clean image from the remaining signal subspace. Linear estimator minimizing image distortion while maintaining the residual noise energy below some given threshold is used to estimate the clean image. Experiments are carried out using synthetically generated data set with controlled statistics and real SAR image of Selangor area in Malaysia. The performance of the proposed technique is compared with Lee and homomorphic wavelet in terms of noise variance reduction and preservation of radiometric edges. The results indicate moderate noise reduction by the proposed filter in comparison to Lee but with a significantly less blurry effect and a comparable performance in terms of noise reduction to wavelet but with less artifacts. The results also show better preservation of edges, texture, and point targets by the proposed filter than both Lee and wavelet and less required computational time.

Performance of Wiener Filter and Adaptive Filter for Noise Cancellation in Real-Time Environment

Form the past several decades’ noise cancellation in speech signal gains researchers’ attention. Several techniques were developed for noise cancellation among them optimal wiener filter can be the one of the most fundamental approach for noise cancellation. Later on adaptive filter was introduced to attain better performance. This paper shows the capacity of wiener filter and adaptive filter for removal of noise by estimating the signal by means of removing the noise signal form the corrupted signal.Wiener filter plays a central role in wide range of applications such as linear prediction, echo cancellation, signal restoration, channel equalization and system identification. In this paper the performance of wiener filter and adaptive filter for removal of noise in the presence of real time environment are compared. In the existing papers the authors have proposed the theory of wiener filter and adaptive filter algorithms in real time environment like recorded speech. So this is paper is going to take the part of the existing paper and going to perform the noise cancellation. In order to measure the performance step size is the main factor for the convergence speed and mean square error. Wiener filter provides better performance for noise cancellation but it requires large no. of computations i.e., complexity and cost of the system is going to increase, so adaptive filter is the alternate approach for removal of noise with moderate complexity and cost. The simulation result clearly shows that wiener filter gives the better performance but due to high cost adaptive filter is the choice of many applications. This paper is going to discuss about wiener filter theory, wiener filter problem, solution to optimal filtering, adaptive filtering, adaptive algorithm, study of wiener filter and adaptive filter for noise reduction etc.

A time-frequency approach for EEG signal segmentation

The record of human brain neural activities, namely electroencephalogram (EEG), is known to be nonstationary in general. In addition, the human head is a non-linear medium for such signals. In many applications, it is useful to divide the EEGs into segments in which the signals can be considered stationary. Here, Hilbert-Huang Transform (HHT), as an effective tool in signal processing is applied since unlike the traditional time-frequency approaches, it exploits the non-linearity of the medium and nonstationarity of the EEG signals. In addition, we use Singular Spectrum Analysis (SSA) in the pre-processing step as an effective noise removal approach. By using synthetic and real EEG signals, the proposed method is compared with Wavelet Generalized Likelihood Ratio (WGLR) algorithm as a well-known signal segmentation method. The simulation results indicate the performance superiority of the proposed method.

A Hybrid Approach for Stripe Noise Removal in Frequency Domain

Stripe noise always occurs in line-scan images which can seriously affect the image quality for future usage. To eliminate the stripe noise from the polluted line-scan images, a hybrid approach for stripe noise removal in frequency domain was presented. Firstly, the mathematical model of the stripe noise was established to describe its character. Secondly, the information of the stripe noise was separated from other information of the image by wavelet decomposition. Then, a band stop filter was designed for removing the stripe noise from the separated details band in spatial-frequency domain of Fourier. At last, the denoised image was obtained after wavelet reconstruction. The results indicate that the proposed approach can remove the stripe noise effectively from the polluted line-scan image while preserving as much details of the object in the image as possible.

A Novel Approach for Mixed Noise Removal using 'ROR' Statistics Combined WITH ACWMF and DPVM

In this paper a mixed noise removal framework using Robust Outlyingness ratio (ROR) statistics combined with adaptive center weighted median and detail preserving variational approach is discussed. The pixels are classified into different clusters based on the ROR statistics, which measures how impulse like each pixel is .To make the results more accurate, each cluster undergoes coarse and fine stage of noise detection and removal, which make use of ACWMF for noise detection and DPVM for restoration of noise candidates. Final stage of filtering is done by means of Non Local Means filter. Extensive simulations show that the proposed scheme consistently works well in suppressing both impulse and Gaussian noise with different noise ratios.

An Improved Image Denoising Approach Using Optimized Variance-Stabilizing Transformations

Image denoising plays a very important process in image processing. Many researchers would study about the various noise removal approaches used for fluorescence images. The fluorescence images mainly contain Poisson noise. The process of denoising is the image is gaussianized first then the resulting image is given as an input to the OWT SURELET to remove the Gaussian white noise. The OWT SURELET is one of the conventional denoising algorithms used to remove the noises. Then to attain an exact signal an inverse transform is applied to the denoised signal. Difficulties may arise to choose the inverse transformation for fluorescence images because the bias error may occur if the non linear forward transform is applied. To overcome these difficulties a study is made on the proposed Anscombe transformation and OWT-SURELET suitable for fluorescence images. The proposed OWT-SURELET is compared with BLS_GSM strategy and the results are discussed. Experimental result shows that the proposed system is more efficient than the existing system, the results are tested using ISNR changes with the denoising algorithms and the inverse transforms.

A Novel Approach for Salt and Pepper Noise Removal using ROR and Contourlet Transform

ROR and Contourlet transform is proposed to restore images corrupted by salt and pepper impulse noise. The operation is carried out in two stages, i.e., detection followed by filtering. For detection first, we propose the robust outlyingness ratio (ROR) for measuring how impulse like each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, proposed algorithm using contourlet transform (CT). Simulation results demonstrate that the proposed algorithm is better than traditional filters and is particularly effective for the cases where the images are very highly corrupted.

A new approach for high density saturated impulse noise removal using decision-based coupled window median filter

A new decision-based algorithm has been proposed for the restoration of digital images which are highly contaminated by the saturated impulse noise (i.e., salt-and-pepper noise). The proposed denoising algorithm performs filtering operation only to the corrupted pixels in the image, keeping uncorrupted pixels intact. The present study has used a coupled window scheme for the removal of high density noise. It has used sliding window of increasing dimension, centered at any pixel and replaced the noisy pixels consecutively by the median value of the window. However, if the entire pixels in the window are noisy, then the dimension of sliding window is increased in order to obtain the noise-free pixels for median calculation. Consequently, this algorithm has been found to be able to remove the high density salt-and-pepper noise and also preserved the fine details of the four images, Lena, Elaine, Rhythm, and Sunny, used as test images in this study (The latter two real-life images have been acquired using Sony: Steady Shot DSC- S3000). Experimentally, it has been found that the proposed algorithm yields better peak signal-to-noise ratio, image enhancement factor, structural similarity index measure and image quality index, compared with the other state-of-art median-based filters viz. standard median filter, adaptive median filter, progressive switched median filter, modified decision-based algorithm and modified decision-based unsymmetric trimmed median filter.

An ANN Based Two Pass-Two Phase Adaptive Filtering of a Digital Image Corrupted by SPN

Images get contaminated due to different noises at various stages of processing and Salt and Pepper is one such noise. The noise removal approach used for filtering mainly differs in their basic methodologies, but the purpose is to suppress different types and percentage of noise. Some of the filtering schemes replace those corrupted pixels by indentifying the positions of the corrupted pixels in the observed noisy image, with the help of a noise detector, whereas others remove all the pixels irrespective of corruption. This paper investigates the former method in denoising a digital image through incorporation of an adaptive threshold into the noise detection process. The adaptive threshold value thus obtained is based on the noisy image characteristics and their statistics using LeNN (Legendre Neural Network) and the patterns of input image are taken to train FLANN (Functional Link Artificial Neural Network) corrupted by SPN(Salt & Pepper noise). Comparative analysis on standard images at different noise percentage shows that the proposed scheme outperforms the existing schemes in terms of PSNR (peak signal to noise ratio). Thus, the proposed method named as “Two passTwo phase adaptive filtering mechanism” is feasible and also makes it an efficient filter to restore the gray image fairly well preserving the quality of the filtered image, and also provides a better visual perception. General Terms Neural network, Image, Noise