Sign Summation Image and Its Application to Robust Image Registration
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This paper proposes sign summation (SS) images for robust image registration against noises. An SS image is generated as the summation of the signs of the differences between the focused pixel and some of its neighboring ones. For image registration in subpixel accuracy, phase-only correlation or cross-correlation is applied to SS images rather than nonprocessed images. Experimental rusults show that SS imaging improves estimation accuracy, whereas median filter, a well-known impulse noise suppression filter, degrades the accuracy.Keywords:
Subpixel rendering
Image registration
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Impulse noise
Salt-and-pepper noise
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To reduce the loss of image details caused by traditional median filter,an adaptive median filter algorithm is proposed based on multi-stage noise detection.According to the spatial correlation of pixels,the proposed algorithm detected the types of noise progressively.First,it detected the single noise pixel by counting the number of pixels that had similar values in filter window.Then it extended the filter window in adjacent space to detect the two pixels noise,and added constraints to detect the three or above adjacent pixels noise.Finally,the noise pixel was replaced with the median.In addition,the proposed algorithm can adjust the threshold of pixel spatial correlation discrimination adaptively to meet the processing of different distribution noises.Compared with other median filters,experimental results show that the proposed algorithm can remove noises effectively with maintaining the detail information of the original image.
Salt-and-pepper noise
Image noise
Nonlinear filter
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Since the medical image is usually corrupted by noise, the filter method is applied to remove the noise and improve the image quality. In this paper, a modified adaptive median filter method is proposed for filtering the medical images. When identifying noises, by selecting the maximum and the minimum gray values in the image as a criterion of judging the noise pixels, the probability that a nonnoise pixel is misjudged to be a noisy one is reduced, and the processing time for finding the maximum and minimum gray values in each local window is drastically decreased as well. When filtering the image, according to the noise granularity function (NGF) in a 3×3 window, the filtering window size is adaptively adjusted, then the median filter is used to eliminate the current noise-marked pixel in the median image (MI) generated by the adaptive median filter, and at the same time the noise mark is cancelled. The proposed method may both effectively remove the noises, and preserve image detail information well. The experimental results reveal that the proposed method is particularly effective in filtering the impulse noises, also called salt-and-pepper noises superimposed on images, including computed tomography (CT) and magnetic resonance (MR) images.
Salt-and-pepper noise
Impulse noise
Image noise
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A critical issue in image restoration is the problem of Gaussian noise removal while keeping the integrity of relevant image information. Clinical magnetic resonance imaging (MRI) data is normally corrupted by Rician noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. The quality of ultrasound (US) imaging is degraded by the presence of signal dependant noise known as speckle. It generally tends to reduce the resolution and contrast, thereby, to degrade the diagnostic accuracy of this modality. For this reasons, denoising methods are often applied to increase the: Signal-to-Noise Ratio (SNR) and improve image quality. This paper proposes a statistical filter, which is a modified version of Hybrid Median filter for noise reduction, which computes the median of the diagonal elements and the mean of the diagonal, horizontal and vertical elements in a moving window and finally the median value of the two values will be the new pixel value. The results show that our proposed method outperforms the classical implementation of the Mean, Median and Hybrid Median filter in terms of denoising quality. Comparison with well established methods, such as Total Variation, Wavelet and Wiener filters show that the proposed filter produces better denoising results, preserving the main structures and details.
Speckle noise
Non-Local Means
Rician fading
Salt-and-pepper noise
Gaussian Noise
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A new fast and reliable hybrid switching impulse noise filter is proposed for highly corrupted images, especially biomedical images. A combination of switching mean and median filtering technique is used to remove impulse noise from corrupted images. This efficient filtering technique is implemented as a two stage algorithm: In the first stage, identification of corrupted pixels that are to be filtered are perfectly detected into a flag image using an iterative fixed sized smaller window approach; In the second stage, using the detected flag image, the pixels to be modified are identified and corrected by the proposed hybrid filter. Experimental results have shown that the proposed algorithm performs far more superior than many of the traditional median filtering and mean filtering techniques reported so far. Since biomedical images are having salt and pepper like structures in it, the ability of the proposed filter to distinguish it from actual noise has been checked. The Peak Signal to Noise Ratio (PSNR) of the filtered image using the proposed technique is much higher than that of the filtered images obtained by the existing mean and median filtering techniques.
Impulse noise
Salt-and-pepper noise
Non-Local Means
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Images are normally degraded by some form of impulse noises during the acquisition, transmission and storage in the physical media. Most of the real time applications usually require bright and clear images, hence distorted or degraded images need to be processed to enhance easy identification of image details and further works on the image. In this paper we have analyzed and tested the number of existing median filtering algorithms and their limitations. As a result we have proposed a new effective noise adaptive median filtering algorithm, which removes the impulse noises in the color images while preserving the image details and enhancing the image quality. The proposed method is a spatial domain approach and uses the 3×3 overlapping window to filter the signal based on the correct selection of neighborhood values to obtain the effective median per window. The performance of the proposed effective median filter has been evaluated using MATLAB, simulations on a both gray scale and color images that have been subjected to high density of corruption up to 90% with impulse noises. The results expose the effectiveness of our proposed algorithm when compared with the quantitative image metrics such as PSNR, MSE, RMSE, IEF, Time and SSIM of existing standard and adaptive median filtering algorithms.
Impulse noise
Peak signal-to-noise ratio
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Concerning the fuzzy and unclear details after color image denoising,the correlation properties of the adjacent pixels as well as the correlation among the color channels were analyzed.First,one-step singular correlation detection algorithm was used to detect the noises of each layer of the pre-treatment color image,and then the most relevant vector median was used to fill value of the noises.Finally,the color image filtering processing was realized.The experimental results show that this method not only accurately detects the salt-pepper noises,but also well restores and protects the original information such as the edge details.The color image filtering accuracy and performance criterion such as Peak-Signal-to Noise Ratio(PSNR) are further improved.
Peak signal-to-noise ratio
Singular value
Salt-and-pepper noise
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Impulse noise removal is considered one of the most burning topic in digital image processing (DIP). When an image is formed, factors like lighting (source, and intensity) and camera characteristics like the sensor response, lenses and also atmospheric condition affect the presence of the image. It hides the important fine points and information of images. In order to enhance the qualities of the image, the removal of noises becomes imperative and that should not be at the cost of any loss of image information like edges. Removal of noise is one of the most important pre-processing tasks of different of image analysis works and tasks like image enhancement, steganography, segmentation and other enhancement related process. In this research article, we have proposed a new method for the removal and restoration of gray images is introduced, when images are corrupted by impulse noise. This method proposed a novel combination of Mean. Median and trimmed value concept for elimination of fixed valued impulse noise. Our methodology ensures a better performance for different level low, medium and high density of fixed value impulse noise as compare to the other famous filters like Standard Median Filter (SMF), Decision Based Median Filter (DBMF) and Modified Decision Based Median Filter (MDBMF) etc. The main objective of the proposed method is to improve not only a peak signal to noise ratio (PSNR) but also improve the visual perception and reduction in blurring of the resultant image. In the proposed method when previous pixels values, 0's and 255's are present in the particular window and all the pixel values are 0's and 255's then the remaining corrupted pixels are substituted by mean and median value. Proposed methodology was tested on gray-scale images like Mandrill and Lena. The experimental result shows improved value of peak signal to noise ratio (PSNR) and mean square error (MSE) values with better visual and human perception.
Impulse noise
Salt-and-pepper noise
Image noise
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In this paper a novel, efficient extension of the vector median filter intended for the suppression of impulsive noise in color images is proposed. The new filter operates on the trimmed distances between color pixels belonging to the filtering window. The cumulated distances calculated for each pixel in the local window is used to perform the reduced vector ordering, which allows to find the pixel which is centrally located in the cluster of most similar samples. The introduced generalization allows to improve the effectiveness of the standard vector median filter and can be used for more efficient restoration of color images distorted by high intensity impulsive noise. The unique property of the described filtering framework is its ability to sharpen the image edges which was quantified using a novel image restoration measure. Additionally, the proposed vector median extension does not increase its computational intensity, which allows to use it in real time applications.
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A method for reducing speckle noise in medical ultrasonic images is presented. It is called the adaptive weighted median filter (AWMF) and is based on the weighted median, which originates from the well-known median filter through the introduction of weight coefficients. By adjusting the weight coefficients and consequently the smoothing characteristics of the filter according to the local statistics around each point of the image, it is possible to suppress noise while edges and other important features are preserved. Application of the filter to several ultrasonic scans has shown that processing improves the detectability of small structures and subtle gray-scale variations without affecting the sharpness or anatomical information of the original image. Comparison with the pure median filter demonstrates the superiority of adaptive techniques over their space-invariant counterparts. Examples of processed images show that the AWMF preserves small details better than other nonlinear space-varying filters which offer equal noise reduction in uniform areas.< >
Speckle noise
Smoothing
Weighted median
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