SNORE: spike noise removal and detection

A method for detection and removal of random spike noise in magnetic resonance (MR) raw data (k-space data) is described. This method would reduce or eliminate the corduroy-type and higher than usual level artifacts in MR images resulting from random spike noise in k-space data. The method described involves applying a spatially varying threshold to be k-space data. Any data point that has a magnitude greater than that of the threshold value at that location will be replaced by a local complex average of the neighboring data points or some other suitable data replacement scheme.     

Restoration of lossy compressed noisy images

Noise degrades the performance of any image compression algorithm. However, at very low bit rates, image coders effectively filter noise that may he present in the image, thus, enabling the coder to operate closer to the noise free case. Unfortunately, at these low bit rates the quality of the compressed image is reduced and very distinctive coding artifacts occur. This paper proposes a combined restoration of the compressed image from both the artifacts introduced by the coder along with the additive noise. The proposed approach is applied to images corrupted by data-dependent Poisson noise and to images corrupted by film-grain noise when compressed using a block transform-coder such as JPEG. This approach has proved to be effective in terms of visual quality and peak signal-to-noise ratio (PSNR) when tested on simulated and real images.     

脉冲噪声图像的中-均值滤波算法

由于在图像信息的获取和传输过程中,图像常常受到不同程度的脉冲噪声污染。为了有效地去除高浓度脉冲噪声,提出了一种基于中－均值滤波器的噪声去除算法。该方法根据脉冲噪声特点,设定一个简单的噪声检测算子,根据噪声检测结果设定自适应滤波窗口,同时根据噪声密度选择中值和均值滤波器。为了更加有效地保留图像的原有信息,对非噪声点不做滤波处理。仿真结果表明,所提出的中－均值滤波方法不仅能有效地去除高浓度的脉冲噪声,而且能很好地保留图像的原有信息,并具有较短的滤波处理时间。     

Erratum to: Multi-scale directional-filtering-based method for follicular lymphoma grading

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.     

Assessment of averaging spatially correlated noise for 3-D radial imaging

Any measurement of signal intensity obtained from an image will be corrupted by noise. If the measurement is from one voxel, an error bound associated with noise can be assigned if the standard deviation of noise in the image is known. If voxels are averaged together within a region of interest (ROI) and the image noise is uncorrelated, the error bound associated with noise will be reduced in proportion to the square root of the number of voxels in the ROI. However, when 3-D-radial images are created the image noise will be spatially correlated. In this paper, an equation is derived and verified with simulated noise for the computation of noise averaging when image noise is correlated, facilitating the assessment of noise characteristics for different 3-D-radial imaging methodologies. It is already known that if the radial evolution of projections are altered such that constant sampling density is produced in k-space, the signal-to-noise ratio (SNR) inefficiency of standard radial imaging (SR) can effectively be eliminated (assuming a uniform transfer function is desired). However, it is shown in this paper that the low-frequency noise power reduction of SR will produce beneficial (anti-) correlation of noise and enhanced noise averaging characteristics. If an ROI contains only one voxel a radial evolution altered uniform k-space sampling technique such as twisted projection imaging (TPI) will produce an error bound ~35% less with respect to noise than SR, however, for an ROI containing 16 voxels the SR methodology will facilitate an error bound ~20% less than TPI. If a filtering transfer function is desired, it is shown that designing sampling density to create the filter shape has both SNR and noise correlation advantages over sampling k-space uniformly. In this context SR is also beneficial. Two sets of 48 images produced from a saline phantom with sodium MRI at 4.7T are used to experimentally measure noise averaging characteristics of radial imaging and good agreement with theory is obtained.     

Image Denoising Using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial Domain

This correspondence proposes an efficient algorithm for removing Gaussian noise from corrupted image by incorporating a wavelet-based trivariate shrinkage filter with a spatial-based joint bilateral filter. In the wavelet domain, the wavelet coefficients are modeled as trivariate Gaussian distribution, taking into account the statistical dependencies among intrascale wavelet coefficients, and then a trivariate shrinkage filter is derived by using the maximum a posteriori (MAP) estimator. Although wavelet-based methods are efficient in image denoising, they are prone to producing salient artifacts such as low-frequency noise and edge ringing which relate to the structure of the underlying wavelet. On the other hand, most spatial-based algorithms output much higher quality denoising image with less artifacts. However, they are usually too computationally demanding. In order to reduce the computational cost, we develop an efficient joint bilateral filter by using the wavelet denoising result rather than directly processing the noisy image in the spatial domain. This filter could suppress the noise while preserve image details with small computational cost. Extension to color image denoising is also presented. We compare our denoising algorithm with other denoising techniques in terms of PSNR and visual quality. The experimental results indicate that our algorithm is competitive with other denoising techniques.     

An improved recursive and adaptive median filter for high density impulse noise

An improved recursive and adaptive median filter (RAMF) for the restoration of images corrupted with high density impulse noise is proposed in the present paper. Adaptive operation of the filter is justified with the variation in size of working window which is centered at noisy pixels. Based on the presence of noise-free pixel(s), the size of working window changes. The noisy pixels are filtered through the replacement of their values using both noise-free pixels of the current working window and previously processed noisy pixels of that window. These processed noisy pixels are obtained recursively. The combined effort thus provides an improved platform for filtering high density impulse noise of images. Experimental results with several real-time noisy images show that the proposed RAMF outperforms other state-of-the-art filters quantitatively in terms of peak signal to noise ratio (PSNR) and image enhancement factor (IEF). The superiority of the filter is also justified qualitatively through visual interpretation.     

Variational level set method for image segmentation with simplex constraint of landmarks

Landmarks are prior image features for a variety of computer vision tasks. In the image processing domain, research on image segmentation methods has always been a significant topic. Due to the image characteristics of heterogeneous nature, lack of clear boundaries, noise and so on, accurate segmentation of the image is still a challenge. In this paper, utilizing a level set framework and the simplex constraint, preferred image point landmarks are combined into a variational segmentation model to enforce the contour evolve with prior points. Then the alternating minimization algorithm of the proposed model is designed, meanwhile the landmarks constraints are doubled ensured with simplex projection. Finally, experiments on many synthetic and real-world images were implemented. Comparing with other state-of-the-art segmentation variational models, the most striking result to emerge from the data is that the proposed method has higher segmentation performance. Benefiting from appropriate point landmarks, the proposed segmentation method can tackle noisy, weak edges and corrupted area images effectively and robustly.     

An effective 2-stage method for removing impulse noise in images

In this paper, a robust 2-stage impulse noise removal system is proposed to remove impulse noise from extremely corrupted images. The contributions are in two-fold. First, a neuro-fuzzy based impulse noise detector (NFIDET) is introduced to identify the noisy pixels. NFIDET is a powerful noise detector that can handle image corruption even up to 90% with zero miss and false detection rate with a simple neuro-fuzzy structure. This is the best result among the other impulse noise detectors in the literature. Second, this paper presents a new approach for weight calculation of adaptive weighted mean filter by using robust statistical model. An adaptive robust weighted mean (ARWM) filter removes a detected noisy pixel by adaptively determining filtering window size and replacing a noisy pixel with the weighted mean of the noise-free pixels in its window. A Geman–McClure robust estimation function is used to estimate the weights of the pixels. Simulation results also show that the proposed robust filter substantially outperforms many other existing algorithms in terms of image restoration.     

A new adaptive switching filter with directional impulse detection

A novel adaptive switching filter （ASF） based on directional detection is proposed for denoising the images that are highly corrupted by impulse noise. The proposed algorithm employs an efficient noise detection mechanism. It first employs an efficient method to estimate the differences between the current pixel and its neighbors aligned with 28 directions. The current noise pixel is replaced by a median or a mean value within an adaptive filter window with respect to different noise densities. Experimental results show that the proposed approach can not only achieve very low miss-detection ratio and false-alarm ratio even up to high noise corruption, but also preserve the detailed information of an image very well.     

Prescanned minmax centre-weighted filters for image restoration

The prescanned minmax centre-weighted (PMCW) filter, which is capable of restoring images severely corrupted by impulsive noise, is presented. Before filtering, the input image is scanned by a running window; the maximum and minimum of each ranked set in the running window are grouped as the first subset, and the rest as the second subset. Then, the two subsets are filtered in sequence by a centered weight filter. It is shown that filtering results in the first subset provide the extension property by which the effective smoothing region for the subsequent filtering in the second subset is extended. The detail-control property of the PMCW is characterised and some relationships between PMCW and ranked-order based filters are derived. Quantitative comparisons demonstrate that the PMCW filter offers a more desirable combination of noise suppression and detail preservation properties than can other median-type filters     

Restoration of embedded image from corrupted stego image

Digital images are easily corrupted by noise during transmission. This paper presents a robust method for restoring embedded image from corrupted stego image. We firstly identify unreliable bits of each pixel in the embedded image by detecting noise in the stego image. Secondly, we calculate the distortion value and credibility value of each pixel, and use them to determine corrupted pixels. For each corrupted pixel, we correct its value by adjusting the unreliable bits. Experimental results show that the proposed method can restore embedded image with good visual quality.     

A new efficient approach for the removal of impulse noise fromhighly corrupted images

A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window. As part of this framework, several algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the algorithm switches between the output of an identity filter and a rank-ordered mean (ROM) filter. The technique achieves an excellent tradeoff between noise suppression and detail preservation with little increase in computational complexity over the simple median filter. For a small additional cost in memory, this simple strategy is easily generalized into a multistate approach using weighted combinations of the identity and ROM filter in which the weighting coefficients can be optimized using image training data. Extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as 40% impulse noise corruption. Moreover, the method can effectively restore images corrupted with Gaussian noise and mixed Gaussian and impulse noise. Finally, the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise.     

Fast switching based median–mean filter for high density salt and pepper noise removal

This paper proposes a fast switching based median–mean filter for high density salt and pepper noise in images. The extreme minimum value and extreme maximum value of the noisy image are used to identify the noise pixels. In the filtering stage, the corrupted pixel is replaced either by median value or mean value based on the number of noise free pixels in the filtering window. The qualitative and quantitative results show that the proposed filter outperforms the other switching based filters namely ACWMF, PSMF, AMF, DBA and MDBUTMF in terms of noise removal and edge preservation for noise densities varying from 10% to 90%.     

Using an MRI distortion transfer function to characterize the ghosts in motion-corrupted images

Motion artefact suppression remains an active topic in MRI. In this paper, we suggest that certain nonrigid, or spatially variant, characteristics of motion of an object can be represented by extending the work of Mitsa et al. This empirical extension uses a ghost distortion transfer function (GTDF) applied to the k-space (frequency domain) data. We demonstrate the variety of ghost characteristics that can be generated from various two-dimensional (2-D) GTDF's. The distortion transfer function for periodic motion along the Z-axis can be determined from the nonoverlapped portions of the ghost and central image. It required a GDTF with the shape of a belt bandpass filter to produce an image corresponding to the ghosts of a volunteer's abdomen image corrupted by unknown respiratory motion artefacts. The preliminary results of a composite method of motion artefact suppression are presented. The artefact suppression was successful for ghost images described by a GDTF have a low-pass nature, but less successful with ghosts have a GDTF of a bandpass nature.     

A switching median filter with boundary discriminative noise detection for extremely corrupted images.

A novel switching median filter incorporating with a powerful impulse noise detection method, called the boundary discriminative noise detection (BDND), is proposed in this paper for effectively denoising extremely corrupted images. To determine whether the current pixel is corrupted, the proposed BDND algorithm first classifies the pixels of a localized window, centering on the current pixel, into three groups--lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. The center pixel will then be considered as "uncorrupted," provided that it belongs to the "uncorrupted" pixel group, or "corrupted." For that, two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy--in our case, achieving zero miss-detection rate while maintaining a fairly low false-alarm rate, even up to 70% noise corruption. Four noise models are considered for performance evaluation. Extensive simulation results conducted on both monochrome and color images under a wide range (from 10% to 90%) of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median-based filters, in terms of suppressing impulse noise while preserving image details, and yet, the proposed BDND is algorithmically simple, suitable for real-time implementation and application.     

Predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector

A predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector (PASMF) is presented. The PASMF has a noise detector stage and a noise filtering stage. The noise detector implemented using feed forward neural network detects impulse noises in the corrupted image. The filter is a modified median filter, which removes detected impulse noise from the image. In contrast to the standard median filter, the PASMF computes the median value after predicting the appropriate values for neighboring corrupted pixels of the current central pixel in the filtering window. The results show that the PASMF gives better performance visually as well as in terms of different performance measures.     

A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise.

A new operator for restoring digital images corrupted by impulse noise is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filter, an edge detector, and a neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over most other operators is that it offers excellent line, edge, detail, and texture preservation performance while, at the same time, effectively removing noise from the input image. Extensive simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.     

A new fuzzy-based decision algorithm for high-density impulse noise removal

This paper proposes a new efficient fuzzy-based decision algorithm (FBDA) for the restoration of images that are corrupted with high density of impulse noises. FBDA is a fuzzy-based switching median filter in which the filtering is applied only to corrupted pixels in the image while the uncorrupted pixels are left unchanged. The proposed algorithm computes the difference measure for each pixel based on the central pixel (corrupted pixel) in a selected window and then calculates the membership value for each pixel based on the highest difference. The algorithm then eliminates those pixels from the window with very high and very low membership values, which might represent the impulse noises. Median filter is then applied to the remaining pixels in the window to get the restored value for the current pixel position. The proposed algorithm produces excellent results compared to conventional method such as standard median filter (SMF) as well as some advanced techniques such as adaptive median filters (AMF), efficient decision-based algorithm (EDBA), improved efficient decision-based algorithm (IDBA) and boundary discriminative noise detection (BDND) switching median filter. The efficiency of the proposed algorithm is evaluated using different standard images. From experimental analysis, it has been found that FBDA produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measures such as Image Quality Index (IQI).     

Switching impulse noise filter based on Laplacian convolution and pixels grouping for color images

This paper presents a new switching filter consisting of three steps to restore color images corrupted by impulse noise. Firstly, Laplacian convolution is performed on pixels in four directions to mark the pixels which are radically different in value from neighboring pixels as noise candidates. Secondly, those missed neighboring pixels involved in the step of pixels grouping decrease the occurrence of false detection. Pixels in the observation window are separated into noisy pixels and normal pixels with a dividing threshold, whose value is assigned according to a noise density estimator. Finally, a modified arithmetic mean filter is applied to restore the polluted image. Extensive experiments show that the proposed method achieves better performance than comparative methods in terms of peak-signal-to-noise ratio and structural similarity. The proposed method can effectively remove impulse noise in which noise density is varying from 10 to 80%.