Showing papers on "Median filter published in 2005"

Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

Abstract: This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.

Vector filtering for color imaging

Abstract: Vector processing operations use essential spectral and spatial information to remove noise and localize microarray spots. The proposed fully automated vector technique can be easily implemented in either hardware or software; and incorporated in any existing microarray image analysis and gene expression tool.

Robust background subtraction with foreground validation for urban traffic video

Abstract: Identifying moving objects in a video sequence is a fundamental and critical task in many computer-vision applications. Background subtraction techniques are commonly used to separate foreground moving objects from the background. Most background subtraction techniques assume a single rate of adaptation, which is inadequate for complex scenes such as a traffic intersection where objects are moving at different and varying speeds. In this paper, we propose a foreground validation algorithm that first builds a foreground mask using a slow-adapting Kalman filter, and then validates individual foreground pixels by a simple moving object model built using both the foreground and background statistics as well as the frame difference. Ground-truth experiments with urban traffic sequences show that our proposed algorithm significantly improves upon results using only Kalman filter or frame-differencing, and outperforms other techniques based on mixture of Gaussians, median filter, and approximated median filter.

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

Abstract: It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.

Block-based noise estimation using adaptive Gaussian filtering

Abstract: This paper proposes a fast noise estimation algorithm using a Gaussian filter. It is based on block-based noise estimation, in which an input image is assumed to be contaminated by the additive white Gaussian noise and a filtering process is performed by an adaptive Gaussian filter. Coefficients of a Gaussian filter are selected as functions of the standard deviation of the Gaussian noise that is estimated from an input noisy image. For estimation of the amount of noise (i.e., standard deviation of the Gaussian noise), we split an image into a number of blocks and select smooth blocks that are classified by the standard deviation of intensity of a block, where the standard deviation is computed from the difference of the selected block images between the noisy input image and its filtered image. In the experiments, the performance of the proposed algorithm is compared with that of the three conventional (block-based and filtering-based) noise estimation methods. Experiments with several still images show the effectiveness of the proposed algorithm. The proposed noise estimation algorithm can be efficiently applied to noise reduction in commercial image - or video-based applications such as digital cameras and digital television (DTV) for its performance and simplicity.

Fast detection and impulsive noise removal in color images

Abstract: In this paper, a novel approach to the impulsive noise removal in color images is presented. The proposed technique employs the switching scheme based on the impulse detection mechanism using the so-called peer group concept. Compared to the vector median filter and other commonly used multichannel filters, the proposed technique consistently yields very good results in suppressing both the random and fixed-valued impulsive noise. The main advantage of the proposed noise detection framework is its enormous computational speed, which enables efficient filtering of color images in real-time applications.

Imaging device and image processing program for estimating fixed pattern noise from partial noise output of available pixel area

Abstract: An imaging device of the present invention includes an image capturing unit, a noise obtaining unit, a fixed noise calculating unit, and a noise eliminating unit. The image capturing unit generates image data by photoelectrically converting, pixel by pixel, a subject image formed on an available pixel area of a light-receiving surface. The noise obtaining unit reads a noise output from a partial area of the available pixel area. The fixed noise calculating unit calculates an estimation of fixed pattern noise of the available pixel area based on the noise output read from the partial area. The noise eliminating unit subtracts the fixed pattern noise from the image data.

Image deblurring in the presence of salt-and-pepper noise

Abstract: The problem of image deblurring in the presence of salt and pepper noise is considered. Standard image deconvolution algorithms, that are designed for Gaussian noise, do not perform well in this case. Median type filtering is a common method for salt and pepper noise removal. Deblurring an image that has been preprocessed by median-type filtering is however difficult, due to the amplification (in the deconvolution stage) of median-induced distortion. A unified variational approach to salt and pepper noise removal and image deblurring is presented. An objective functional that represents the goals of deblurring, noise-robustness and compliance with the piecewise-smooth image model is formulated. A modified L1 data fidelity term integrates deblurring with robustness to outliers. Elements from the Mumford-Shah functional, that favor piecewise smooth images with simple edge-sets, are used for regularization. Promising experimental results are shown for several blur models.

A Statistically-Switched Adaptive Vector Median Filter

Abstract: This paper presents a new cost-effective, adaptive multichannel filter taking advantage of switching schemes, robust order-statistic theory and approximation of the multivariate dispersion. Introducing the statistical control of the switching between the vector median and the identity operation, the developed filter enhances the detail-preserving capability of the standard vector median filter. The analysis and experimental results reported in this paper indicate that the proposed method is capable of detecting and removing impulsive noise in multichannel images. At the same time, the method is computationally efficient and provides excellent balance between the noise attenuation and signal-detail preservation. Excellent performance of the proposed method is tested using standard test color images as well as real images related to emerging virtual restoration of artworks.

Method of cascading noise reduction algorithms to avoid speech distortion

Abstract: A method of reducing noise by cascading a plurality of noise reduction algorithms is provided. A sequence of noise reduction algorithms are applied to the noisy signal. The noise reduction algorithms are cascaded together, with the final noise reduction algorithm in the sequence providing the system output signal. The sequence of noise reduction algorithms includes a plurality of noise reduction algorithms that are sufficiently different from each other such that resulting distortions and artifacts are sufficiently different to result in reduced human perception of the artifact and distortion levels in the system output signal.

Noise-adaptive spatio-temporal filter for real-time noise removal in low light level images

Abstract: Noise reduction gradually becomes one of the most important features in consumer cameras. The video signal is easily interfered by noise during acquisition process especially in low light environment. Many of the state-of-the-art filters for noise reduction perform-well for high contrast images. However, for low light images, the filter performance degrades seriously. In this paper, we propose a noise-adaptive spatio-temporal (NAST) filtering for removal of noise in low light level images. The proposed algorithm consists of a statistical domain temporal filter (SDTF) for moving area and a spatial hybrid filter (SHF) for stationary area. By minimizing required resources for implementation, we present a high quality, low-cost noise reduction filter for low light images. Since the proposed algorithm is designed for real-time implementation, it can be used as a pre-filter for a DCT-based encoder to enhance the coding efficiency of many commercial applications such as low cost camcorders, digital cameras, CCTV, and surveillance video systems.

Novel FPGA-based implementation of median and weighted median filters for image processing

Abstract: An efficient hardware implementation of a median filter is presented. Input samples are used to construct a cumulative histogram, which is then used to find the median. The resource usage of the design is independent of window size, but rather, dependent on the number of bits in each input sample. This offers a realisable way of efficiently implementing large-windowed median filtering, as required by transforms such as the Trace Transform. The method is then extended to weighted median filtering. The designs are synthesised for a Xilinx Virtex II FPGA and the performance and area compared to another implementation for different sized windows. Intentional use of the heterogeneous resources on the FPGA in the design allows for a reduction in slice usage and high throughput.

Adaptive video encoding using a perceptual model

Abstract: A video encoder includes a region detector module that classifies blocks of video frames. An adaptive filter module applies a median filter to a block based upon a block classification assigned by the region detector module. An adaptive quantization module quantizes a block according to a quantization method adaptively determined based upon a block classification assigned by the region detection module. In one example, a video encoder adaptively determines a median filter selected using a block classification. In another example, a video encoder adaptively determines whether to drop an isolated last transform coefficient based on the block classification, and/or applies a dead-zone selected using the block classification.

Impulsive noise removal using threshold Boolean filtering based on the impulse detecting functions

Abstract: A filter for impulsive noise removal is presented here. The problem of impulsive noise elimination is closely connected with the problem of maximal preservation of image edges. To avoid smoothing of the image during filtering, all noisy pixels must be detected. We consider here an approach, which is based on threshold Boolean filtering, where the binary slices of an image, obtained by the threshold decomposition, are processed by the impulse-detecting Boolean functions proposed. These functions provide a possibility of single-pass filtering, because they detect and replace impulses at the same time.

A New Efficient Impulse Detection Algorithm for the Removal of Impulse Noise

Abstract: A new impulse noise detection algorithm is presented, which can successfully remove impulse noise from corrupted images while preserving image details. The impulse detection algorithm is combined with median filtering to achieve noise removal. The main advantage of the proposed algorithm is that it can detect the impulse noise with high accuracy while reducing the probability of detecting image details as impulses. Also, it can be applied iteratively to improve the quality of restored images. It is efficient and low in complexity. Furthermore, it requires no previous training. Extensive experimental results show that the proposed approach significantly outperforms many well-known techniques.

Apparatus and method for adaptive 3D noise reduction

Abstract: A non-iterative 3D processing method and system is disclosed for generic noise reduction. The 3D noise reducer is based on a simple conversion of the five types of noise to equivalent additive noise of varying statistics. The proposed technique comprises also an efficient temporal filtering technique which combines Minimization of Output Noise Variance (MNV) and Embedded Motion Estimation (EME). The proposed temporal filtering technique may be furthermore combined with classical motion estimation and motion compensation for more efficient noise reducer. The proposed technique comprises also a spatial noise reducer which combines Minimum Mean Squared Error (MMSE) with robust and effective shape adaptive windowing (SAW) is utilized for smoothing random noise in the whole image, particularly for edge regions. Another modification to MMSE is also introduced for handling banding effects for eventual excessive filtering in slowly varying regions.

A robust structure-adaptive hybrid vector filter for color image restoration

Abstract: A robust structure-adaptive hybrid vector filter is proposed for digital color image restoration in this paper. At each pixel location, the image vector (i.e., pixel) is first classified into several different signal activity categories by applying a modified quadtree decomposition to luminance component (image) of the input color image. A weight-adaptive vector filtering operation with an optimal window is then activated to achieve the best tradeoff between noise suppression and detail preservation. Through extensive simulation experiments conducted using a wide range of test color images, the filter has demonstrated superior performance to that of a number of well known benchmark techniques, in terms of both standard objective measurements and perceived image quality, in suppressing several distinct types of noise commonly considered in color image restoration, including Gaussian noise, impulse noise, and mixed noise.

Tracking apparatus and methods using image processing noise reduction

Abstract: Improved apparatus and methodology for image processing and object tracking that, inter alia, reduces noise. In one embodiment, the methodology is applied to moving targets such as missiles in flight, and comprises processing sequences of images that have been corrupted by one or more noise sources (e.g., sensor noise, medium noise, and/or target reflection noise). In this embodiment, a multi-dimensional image is acquired for a first time step t; the acquired image is normalized and sampled, and then segmented into target and background pixel sets. Intensity statistics of the pixel sets are determined, and a prior probability image from a previous time step smoothed. The smoothed prior image is then shifted to produce an updated prior image, and a posterior probability image calculated using the updated prior probability. Finally, the position of the target is extracted using the posterior probability image. A tracking system and controller utilizing this methodology are also disclosed.

Apparatus and method for filtering digital image signal

Abstract: An apparatus and method of filtering a digital image signal. The apparatus includes: a noise reduction filter which selectively outputs one of results obtained by temporally and spatially filtering pixel values of pixels of each of frames of an image as a temporal or spatial filtering value in response to magnitudes of the results of temporal and spatial filtering; and a sharpness enhancement filter which highlights and outputs a high pass component of the temporal or spatial filtering value.

Simultaneous noise filtering and super-resolution with second-generation wavelets

Abstract: Wavelet coefficient thresholding is effective in reducing spatial domain noise in wavelet-based super-resolution algorithms. Here, the effect of the threshold level on reconstructed image quality in second-generation wavelet super-resolution is investigated. The choice of optimal threshold involves a tradeoff between noise filtering and blurring introduced by thresholding. A measure based on the singular values of the image matrix is employed as a reliable gauge of generated high-resolution image quality.

Impulsive noise suppression from images with Jarque-Bera test based median filter

Abstract: A novel impulsive noise (IN) eliminator filter, entitled Jarque–Bera test based Median Filter (JM), which shows a high performance at the restoration of images distorted by IN is proposed in this paper. The JM uses statistical methods in order to find out the corrupted pixels more accurately. The JM replaces only those corrupted pixels with the values obtained from standard median filter as explained in the paper. The simulation results reveal that the proposed filter shows better performance than the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise ratio is very high.

Mltistage bilateral noise filtering and edge detection for color image enhancement

Abstract: Removing noise while preserving and enhancing edges is one of the most fundamental operations of image/video processing. When taking pictures with digital cameras, it is frequently found that the color images are corrupted by miscellaneous noise and, hence, noise filtering is necessary. The difficulty is that usually the filtering will reduce the sharpness of the image. On the other hand, optical lens imperfections are usually equivalent to spatial low pass filters and tend to result in blurred images. It is customary to apply edge enhancement algorithm on the image in order to improve the sharpness, but this process usually increase the noise level as a by-product. In this paper, we present a new integrated approach to address these issues. The proposed approach combines color edge detection, bilateral noise filter, and edge enhancement based on suitable color spaces. The experimental results show that the proposed approach can effectively reduce the noise while preserving and enhancing edges.

Bayesian methods for noise reduction in image processing

Abstract: Improved methodology for image processing and object tracking that, inter alia, reduces noise. In one embodiment, the methodology is applied to moving targets, and comprises processing sequences of images that have been corrupted by one or more noise sources (e.g., sensor noise, medium noise, and/or target reflection noise). A likelihood or similar logical construct (e.g., Bayes' rule) is applied to the individual images (or aggregations thereof) of an image sequence in order to generate a posterior image for each observed image. The posterior images are fed-forward to the determination of the posterior image for one or more subsequent images (after smoothing), thereby making these subsequent determinations more accurate. The net result is a more accurate and noise-reduced representation (and location) of the target in each image.

Fast method for noise level estimation and integrated noise reduction

Abstract: This paper describes a fast method for noise level estimation and denoising. Specifically, we address the problem of estimating the standard deviation of additive white Gaussian noise in digital images; the computed value is used to reduce Gaussian noise and eliminate defective pixels in a raw digital image. The method is particularly suitable for implementation in low power mobile devices with imaging capabilities such as camera phones, as well as digital still cameras (DSC).

An intelligent image agent based on soft-computing techniques for color image processing

Abstract: An intelligent image agent based on soft-computing techniques for color image processing is proposed in this paper. The intelligent image agent consists of a parallel fuzzy composition mechanism, a fuzzy mean related matrix process and a fuzzy adjustment process to remove impulse noise from highly corrupted images. The fuzzy mechanism embedded in the filter aims at removing impulse noise without destroying fine details and textures. A learning method based on the genetic algorithm is adopted to adjust the parameters of the filter from a set of training data. By the experimental results, the intelligent image agent achieves better performance than the state-of-the-art filters based on the criteria of Peak-Signal-to-Noise-Ratio (PSNR) and Mean-Absolute-Error (MAE). On the subjective evaluation of those filtered images, the intelligent image agent also results in a higher quality of global restoration.

A new vector median filter based on fuzzy metrics

Abstract: Vector median filtering is a well known technique for reducing noise in color images These filters are defined on the basis of a suitable distance or similarity measure, being the most common used the Euclidean and City-Block distances In this paper, a Fuzzy Metric, in the sense of George and Veeramani (1994), is defined and applied to color image filtering by means of a new Vector Median Filter It is shown that the standard Vector Median Filter is outperformed when using this Fuzzy Metric instead of the Euclidean and City-Block distances

Multiplicative Background-Foreground Estimation Under Uncontrolled Illumination using Intrinsic Images

Abstract: Instead of the conventional background and foreground definition, we propose a novel method that decomposes a scene into time-varying background and foreground intrinsic images. The multiplication of these images reconstructs the scene. First, we form a set of previous images into a temporal scale and compute their spatial gradients. By taking advantage of the sparseness of the filter outputs, we estimate the background by median filtering the gradients, and compute the corresponding foreground using the background. We also propose a robust method to threshold foregrounds to obtain a change detection mask of the moving pixels. We show that a different set of filters can detect the static and moving lines. Computationally, the proposed method is comparable with the state of the art, and our simulations prove the effectiveness of the intrinsic background/foreground decomposition even under sudden and severe illumination changes.

A multichannel weighted median filter for complex array signal processing

Abstract: Summary form only given. Multi-channel and multi-spectral signals are often correlated across channels. Moreover, multispectral images have considerable similarity in in-channel correlations. But in array signal processing, due to the existence of multiple frequency components and their phase shifts, in-channel correlations may vary drastically. In this paper, a new multichannel weighted median filter is proposed which can capture the general correlation structure in array signals and process them in an efficient manner. The algorithm is further extended onto the complex domain by means of "phase coupling". The performance of the filter is presented in a three-sensor array processing example.