Showing papers on "Median filter published in 2012"
TL;DR: A weighted sum based multi-exposure image fusion method which consists of three image features composed of local contrast, brightness and color dissimilarity are first measured to estimate the weight maps refined by recursive filtering to obtain accurate weight maps for image fusion.
Abstract: This paper proposes a weighted sum based multi-exposure image fusion method which consists of two main steps: three image features composed of local contrast, brightness and color dissimilarity are first measured to estimate the weight maps refined by recursive filtering. Then, the fused image is constructed by weighted sum of source images. The main advantage of the proposed method lies in a recursive filter based weight map refinement step which is able to obtain accurate weight maps for image fusion. Another advantage is that a novel histogram equalization and median filter based motion detection method is proposed for fusing multi-exposure images in dynamic scenes which contain motion objects. Furthermore, the proposed method is quite fast and thus can be directly used for most consumer cameras. Experimental results demonstrate the superiority of the proposed method in terms of subjective and objective evaluation.
227 citations
TL;DR: A new fast dehazing method from single image based on filtering that is fast with linear complexity in the number of pixels of the input image and can be further accelerated using GPU, which makes this method applicable for real-time requirement.
Abstract: In this paper, we propose a new fast dehazing method from single image based on filtering. The basic idea is to compute an accurate atmosphere veil that is not only smoother, but also respect with depth information of the underlying image. We firstly obtain an initial atmosphere scattering light through median filtering, then refine it by guided joint bilateral filtering to generate a new atmosphere veil which removes the abundant texture information and recovers the depth edge information. Finally, we solve the scene radiance using the atmosphere attenuation model. Compared with exiting state of the art dehazing methods, our method could get a better dehazing effect at distant scene and places where depth changes abruptly. Our method is fast with linear complexity in the number of pixels of the input image; furthermore, as our method can be performed in parallel, thus it can be further accelerated using GPU, which makes our method applicable for real-time requirement.
202 citations
TL;DR: An improved median filtering algorithm is proposed that reduces the noise and retains the details of the image and the complexity is decreased to O (N), and the performance of noise reduction has effectively improved.
167 citations
01 Jan 2012
TL;DR: A comparison of the effect of CIELAB, HSI and YCbCr color space in the process of disease spot detection is done and threshold can be calculated by applying Otsu method on color component to detect the disease spot.
Abstract: In this research, an algorithm for disease spot segmentation using image processing techniques in plant leaf is implemented. This is the first and important phase for automatic detection and classification of plant diseases. Disease spots are different in color but not in intensity, in comparison with plant leaf color. So we color transform of RGB image can be used for better segmentation of disease spots. In this paper a comparison of the effect of CIELAB, HSI and YCbCr color space in the process of disease spot detection is done. Median filter is used for image smoothing. Finally threshold can be calculated by applying Otsu method on color component to detect the disease spot. An algorithm which is independent of background noise, plant type and disease spot color was developed and experiments were carried out on different "Monocot" and "Dicot" family plant leaves with both, noise free (white) and noisy background.
164 citations
TL;DR: A new and simple multidirectional vector-median filter (MD-VMF) is introduced to separate the blended seismic shot gathers to achieve the highest seismic image quality and for standard prestack processing, such as filtering, statics computation, and velocity analysis.
Abstract: Simultaneous source acquisition technology, also referred to as “blended acquisition,” involves recording two or more shots simultaneously. Despite the fact that the recorded data has crosstalk from different shots, conventional processing procedures can still produce acceptable images for interpretation. This is due to the power of the stacking process using blended data with its increased data redundancy and inherent time delays between various shots. It is still desirable to separate the blended data into single shot gathers and reduce the crosstalk noise to achieve the highest seismic image quality and for standard prestack processing, such as filtering, statics computation, and velocity analysis. This study introduced a new and simple multidirectional vector-median filter (MD-VMF) to separate the blended seismic shot gathers. This method extended the well-known conventional median filter from a scalar implementation to a vector version. More specifically, a vector median filter was applied in...
149 citations
TL;DR: This paper proposes a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means) that produces excellent results and outperforms most existing filters for different noise models.
Abstract: Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). 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 impulselike 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, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.
137 citations
TL;DR: A system that enables processing of full resolution images, and a new algorithm for segmenting the nuclei under adequate control of the expert user are implemented, with promising results.
110 citations
TL;DR: Experimental results show that the proposed approach to efficiently remove background noise by detecting and modifying noisy pixels in an image cannot only efficiently suppress high-density impulse noise, but also can well preserve the detailed information of an image.
110 citations
Proceedings Article•
01 Nov 2012TL;DR: A general method for image contrast enhancement and noise reduction is proposed, developed especially for enhancing images acquired under very low light conditions where the features of images are nearly invisible and the noise is serious.
Abstract: A general method for image contrast enhancement and noise reduction is proposed in this paper. The method is developed especially for enhancing images acquired under very low light conditions where the features of images are nearly invisible and the noise is serious. By applying an improved and effective image de-haze algorithm to the inverted input image, the intensity can be amplified so that the dark areas become bright and the contrast get enhanced. Then, the joint-bilateral filter with the original green component as the edge image is introduced to suppress the noise. Experimental results validate the performance of the proposed approach.
106 citations
TL;DR: A novel adaptive detail-preserving filter based on the cloud model (CM) to remove impulse noise is presented and it is shown that, compared with the traditional switching filters, the CM filter makes a great improvement in image denoising.
Abstract: Uncertainties are the major inherent feature of impulse noise. This fact makes image denoising a difficult task. Understanding the uncertainties can improve the performance of image denoising. This paper presents a novel adaptive detail-preserving filter based on the cloud model (CM) to remove impulse noise. It is called the CM filter. First, an uncertainty-based detector identifies the pixels corrupted by impulse noise. Then, a weighted fuzzy mean filter is applied to remove the noise candidates. The experimental results show that, compared with the traditional switching filters, the CM filter makes a great improvement in image denoising. Even at a noise level as high as 95%, the CM filter still can restore the image with good detail preservation.
94 citations
TL;DR: Bilateral filtering for medical image denoising is a nonlinear and local technique that preserves the features while smoothing the images and removes the additive white Gaussian noise effectively but its performance is poor in removing salt and pepper noise.
Abstract: Medical image processing is used for the diagnosis of diseases by the physicians or radiologists. Noise is introduced to the medical images due to various factors in medical imaging. Noise corrupts the medical images and the quality of the images degrades. This degradation includes suppression of edges, structural details, blurring boundaries etc. To diagnose diseases edge and details preservation are very important. Medical image denoising can help the physicians to diagnose the diseases. Medical images include MRI, CT scan, x-ray images, ultrasound images etc. In this paper we implemented bilateral filtering for medical image denoising. Its formulation & implementation are easy but the performance of bilateral filter depends upon its parameter. Therefore for obtaining the optimum result parameter must be estimated. We have applied bilateral filtering on medical images which are corrupted by additive white Gaussian noise with different values of variances. It is a nonlinear and local technique that preserves the features while smoothing the images. It removes the additive white Gaussian noise effectively but its performance is poor in removing salt and pepper noise.
TL;DR: 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 and produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measuressuch as Image Quality Index (IQI).
Abstract: 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).
TL;DR: A tracking algorithm based on adaptive background subtraction about the video detecting and tracking moving objects is presented and the simulation results by MATLAB show that the adaptive Background subtraction is useful in both detecting andtracking moving objects, and background subtracted algorithm runs more quickly.
TL;DR: In this article, a denoising method for ultrasound medical images, whose quality is degraded by the peculiar phenomenon of speckle noise, is presented, which consists in Gaussian filtering of proper wavelet coefficients of the image, corresponding to vertical and diagonal details.
18 Oct 2012
TL;DR: A maximum likelihood based method for estimating the intermicrophone covariance matrix of the noise impinging on the microphone array and it performs better than existing methods for estimating this noise psd.
Abstract: Multi-microphone speech enhancement systems can often be decomposed into a concatenation of a beamformer, which provides spatial filtering of the noisy signal, and a singlechannel (SC) noise reduction filter, which reduces the noise remaining in the beamformer output. Here, we propose a maximum likelihood based method for estimating the intermicrophone covariance matrix of the noise impinging on the microphone array. The method allows prediction of this co-variance matrix for non-stationary noise sources even in signal regions where the target speech signal is present. Although the noise covariance matrix may have several purposes, we use it in this paper for estimating the power spectral density (psd) of the noise entering the SC filter, as this is important for optimal operation of the SC filter. In simulation experiments with a binaural hearing aid setup in a realistic acoustical scenario, the proposed method performs better than existing methods for estimating this noise psd.
01 Jan 2012
TL;DR: In this paper, a comparative study on six methods of impulsive noise detection and filtering is presented, i.e., median filter, Progressive switching median filter (PSM), Fuzzy switching median, Adaptive median filter and Simple adaptive median filter.
Abstract: Image Noise Suppression is a highly demanded approach in digital imaging systems. Impulsive noise is one such noise, which is frequently encountered problem in acquisition, transmission and processing of images. In the area of image restoration, many state-of-the art filters consist of two main processes, classification (detection) and reconstruction (filtering). Classification is used to separate uncorrupted pixels from corrupted pixels. Reconstruction involves replacing the corrupted pixels by certain approximation technique. In this paper such schemes of impulsive noise detection and filtering thereof are proposed. Here we presents a comparative study on six methods such as median filter, Progressive switching median filter, Fuzzy switching median filter, Adaptive median filter, Simple adaptive median filter and its modified version i.e. Modified Simple Adaptive median filter. Objective evaluation parameters i.e. mean square error; peak signal-to- noise ratio is calculated to quantify the performance of these filters.
TL;DR: In this paper, a multiscale kernel principal component analysis (MSKPCA) based on sliding median filter (SFM) is proposed for fault detection in nonlinear system with outliers.
Abstract: In this paper the multiscale kernel principal component analysis (MSKPCA) based on sliding median filter (SFM) is proposed for fault detection in nonlinear system with outliers. The MSKPCA based on SFM (SFM-MSKPCA) algorithm is first proposed and applied to process monitoring. The advantages of SFM-MSKPCA are: (1) the dynamical multiscale monitoring method is proposed which combining the Kronecker production, the wavelet decomposition technique, the sliding median filter technique and KPCA. The Kronecker production is first used to build the dynamical model; (2) there are more disturbances and noises in dynamical processes compared to static processes. The sliding median filter technique is used to remove the disturbances and noises; (3) SFM-MSKPCA gives nonlinear dynamic interpretation compared to MSPCA; (4) by decomposing the original data into multiple scales, SFM-MSKPCA analyze the dynamical data at different scales, reconstruct scales contained important information by IDWT, eliminate the effects of the noises in the original data compared to kernel principal component analysis (KPCA). To demonstrate the feasibility of the SFM-MSKPCA method, its process monitoring abilities are tested by simulation examples, and compared with the monitoring abilities of the KPCA and MSPCA method on the quantitative basis. The fault detection results and the comparison show the superiority of SFM-MSKPCA in fault detection.
TL;DR: Experimental results show that the proposed algorithm is a simpler and efficient method for clarity improvement and contrast enhancement from a single foggy image, and can be comparable with the state-of-the-art methods, and even has better results than them.
Abstract: The misty, foggy, or hazy weather conditions lead to image color distortion and reduce the resolution and the contrast of the observed object in outdoor scene acquisition. In order to detect and remove haze, this article proposes a novel effective algorithm for visibility enhancement from a single gray or color image. Since it can be considered that the haze mainly concentrates in one component of the multilayer image, the haze-free image is reconstructed through haze layer estimation based on the image filtering approach using both low-rank technique and the overlap averaging scheme. By using parallel analysis with Monte Carlo simulation from the coarse atmospheric veil by the median filter, the refined smooth haze layer is acquired with both less texture and retaining depth changes. With the dark channel prior, the normalized transmission coefficient is calculated to restore fogless image. Experimental results show that the proposed algorithm is a simpler and efficient method for clarity improvement and contrast enhancement from a single foggy image. Moreover, it can be comparable with the state-of-the-art methods, and even has better results than them.
TL;DR: A novel approach to impulse noise reduction in color image by applying the quaternion unit transform theory, an efficient filter that can switch between the vector median filter (VMF) and the identity filter (no filtering operation) is proposed.
22 Jul 2012
TL;DR: Experiments with the real hyperspectral data set indicate that the proposed strategy can work well in both of detail preservation and noise removal, and nonlocal means method is extended from 2-D to 3-D.
Abstract: Noise reduction is one of important processing tasks for hyperspectral imagery (HSI). In this paper, a three-dimensional (3-D) nonlocal means filter is proposed for noise reduction of HSI. Recently, non-local means method attracts many attentions due to its global and local integrated property. Nonlocal algorithm searches the similar image patches in the whole scene to build the mean filter, so that it overcomes the disadvantage of local filter that only local pixels within a small neighbor is used, and the disadvantage of global filter that local structure is ignored. In order to explore the spectral-spatial correlation of HSI, nonlocal means method is extended from 2-D to 3-D. Furthermore, as HSI contains both of signal-independent and signal-dependent noises, variance-stabilizing transformation based on noise estimation is used to make noise reduction under the additive Gaussian noise model. Experiments with the real hyperspectral data set indicate that the proposed strategy can work well in both of detail preservation and noise removal.
TL;DR: An iterative algorithm that truncates the extreme values of samples in the filter window to a dynamic threshold is proposed and the resulting nonlinear filter shows some merits of both the arithmetic mean and the order statistical median.
Abstract: The arithmetic mean and the order statistical median are two fundamental operations in signal and image processing. They have their own merits and limitations in noise attenuation and image structure preservation. This paper proposes an iterative algorithm that truncates the extreme values of samples in the filter window to a dynamic threshold. The resulting nonlinear filter shows some merits of both the fundamental operations. Some dynamic truncation thresholds are proposed that guarantee the filter output, starting from the mean, to approach the median of the input samples. As a by-product, this paper unveils some statistics of a finite data set as the upper bounds of the deviation of the median from the mean. Some stopping criteria are suggested to facilitate edge preservation and noise attenuation for both the long- and short-tailed distributions. Although the proposed iterative truncated mean (ITM) algorithm is not aimed at the median, it offers a way to estimate the median by simple arithmetic computing. Some properties of the ITM filters are analyzed and experimentally verified on synthetic data and real images.
TL;DR: Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.
Abstract: Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.
TL;DR: This paper proposes a robust method to suppress the noise components in digital holography (DH), called SPADEDH (SPArsity DEnoising of Digital Holograms), that does not consider any prior knowledge or estimation about the statistics of the noise.
Abstract: In this paper we propose a robust method to suppress the noise components in digital holography (DH), called SPADEDH (SPArsity DEnoising of Digital Holograms), that does not consider any prior knowledge or estimation about the statistics of the noise. In the full digital holographic process we must mainly deal with two kinds of noise. The first one is an additive uncorrelated noise that corrupts the observed irradiance, the other one is the multiplicative phase noise called speckle noise. We consider both lensless and microscope configurations and we prove that the proposed algorithm works efficiently in all considered cases suppressing the aforementioned noise components. In addition, for digital holograms recorded in lensless configuration, we show the improvement in a display test by using a Spatial Light Modulator (SLM).
01 Apr 2012
TL;DR: From the result it is observed that on comparing with non-fuzzy and fuzzy methods, the proposed method gives better information about the images, which is helpful to the pathologists in accurate diagnosing of diseases.
Abstract: This paper gives a novel scheme using intuitionistic fuzzy set theory to enhance the edges of medical images. Medical images contain lots of uncertainties, as they are poorly illuminated and fuzzy/vague in nature. So, direct segmentation techniques will not produce better results. There are lots of researches on edge enhancement starting from non-fuzzy to fuzzy set, but proper enhancement (highlighting important structures) is not obtained. Enhancement of edges helps in recovering the important structures that are not visible properly. Even minute pathological blood vessels/cells are not visible properly and in that case edge enhancement will enhance these blood vessels/cells. Intuitionistic fuzzy set theory is found suitable in medical image processing as it considers more (two) uncertainties as compared to fuzzy set theory. In the processing phase, image is initially converted to intuitionistic fuzzy image and intuitionistic fuzzy entropy is used to obtain the optimum value of the parameter in the membership and non-membership functions. Then it computes the total variation of the pixels with respect to the median value of the image window (rank order filtering). This enhances the borders or the edges of the image. The resulting image is then segmented (edge detected) using standard Canny's edge detector, when simply using Canny's edge detector does not give better result. From the result it is observed that on comparing with non-fuzzy and fuzzy methods, the proposed method gives better information about the images, which is helpful to the pathologists in accurate diagnosing of diseases.
TL;DR: Experimental results demonstrate the superiority of the proposed method in comparison with some of the existing methods for iris localization based on image statistics.
19 May 2012
TL;DR: This paper first utilizes the median filter for image processing, then refines the skeleton to a single pixel wide and removes the redundancy segments, and in this method, the excellent characteristics of the original algorithm can be maintained and problems in the original algorithms can be solved.
Abstract: Zhang-Suen parallel thinning algorithm with the feature of rapidity and practicality ensures the connectivity of the refined curve. However, the refined skeleton cannot be guaranteed in a single pixel wide, and redundancy segments are generated due to acute angles. This paper is based on Zhang-Suen thinning algorithm and first utilizes the median filter for image processing, then refines the skeleton to a single pixel wide and removes the redundancy segments. In this method, the excellent characteristics of the original algorithm can be maintained and problems in the original algorithm can be solved.
TL;DR: The proposed median-Gaussian filtering framework shows good results for STXM images with the size of power of two, if such parameters as threshold, sizes of the median and Gaussian filters, and size of the low frequency window, have been properly selected.
TL;DR: Experimental results show the superiority of the proposed algorithm in measures of PSNR and SSIM, specifically when the image is corrupted with more than 90% impulse noise.
TL;DR: Two novel approaches for the attenuation of impulsive noise both for invariant and time-varying noise distributions are proposed, based on the on-line estimation of an S α S model of the noise probabilistic description and a simple on- line recursive procedure that reliably estimates amplitude thresholds for outlier detection.
TL;DR: The proposed Iris Localization based on morphological or set theory which is well in shape detection is better than the previous method and is proved by the results of different parameters.
Abstract: This study involves the Iris Localization based on morphological or set theory which is well in shape detection. Principal Component Analysis (P CA) is used for preprocessing, in which the removal of redundant and unwanted data is done. Applications such as Median Filtering and Adaptive thresholding are used for handling the variations i n lighting and noise. Features are extracted using Wavelet Packet Transform (WPT). Finally matching is performed using KNN. The proposed method is better than the previous method and is proved by th e results of different parameters. The testing of t he proposed algorithm was done using CASIA iris database (V1.0) and (V3.0).