Edge Detection In Noisy Images Research Articles

Edge detection using nonlinear structure tensor

Abstract In order to improve the performance of edge detection for noisy images, a new edge detection method based on nonlinear structure tensor is proposed. First, the tensor product of noisy images is calculated. The tensor product is diffused according to the image gradient, which depends on the tensor product itself. Finally, the eigenvalues and eigenvectors of the diffusion tensor product are calculated, and the edges of the image are detected according to the eigenvalues. The method is compared with other methods. The experimental results show that the average number of edge points detected by method 1, method 2, method 3, and this method are 513.7, 530.0, 509.0, and 719.3, respectively. The average detection time of method 1, method 2, method 3, and this method were 65.3, 54.9, 57.3, and 33.6 s, respectively. When the number of edge detection is the largest, the average detection time of this method is significantly smaller than that of the three comparison methods. Therefore, this method is more suitable for edge detection of noisy images, and the performance of this method is better than that of the comparison method and can improve the performance of edge detection of noisy images.

基于分数阶微分的噪声图像NSCT域边缘检测

基于分数阶微分的噪声图像NSCT域边缘检测

On Detection of Faint Edges in Noisy Images.

A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected. In this paper we offer a formalism to study this question and subsequently introduce computationally efficient multiscale edge detection algorithms designed to detect faint edges in noisy images. In our formalism we view edge detection as a search in a discrete, though potentially large, set of feasible curves. First, we derive approximate expressions for the detection threshold as a function of curve length and the complexity of the search space. We then present two edge detection algorithms, one for straight edges, and the second for curved ones. Both algorithms efficiently search for edges in a large set of candidates by hierarchically constructing difference filters that match the curves traced by the sought edges. We demonstrate the utility of our algorithms in both simulations and applications involving challenging real images. Finally, based on these principles, we develop an algorithm for fiber detection and enhancement. We exemplify its utility to reveal and enhance nerve axons in light microscopy images.

Noisy images edge detection: Ant colony optimization algorithm

The edges of an image define the image boundary. When the image is noisy, it does not become easy to identify the edges. Therefore, a method requests to be developed that can identify edges clearly in a noisy image. Many methods have been proposed earlier using filters, transforms and wavelets with Ant colony optimization (ACO) that detect edges. We here used ACO for edge detection of noisy images with Gaussian noise and salt and pepper noise. As the image edge frequencies are close to the noise frequency band, the edge detection using the conventional edge detection methods is challenging. The movement of ants depends on local discrepancy of image’s intensity value. The simulation results compared with existing conventional methods and are provided to support the superior performance of ACO algorithm in noisy images edge detection. Canny, Sobel and Prewitt operator have thick, non continuous edges and with less clear image content. But the applied method gives thin and clear edges.

A new approach for edge detection in noisy images based on the LPGPCA technique

In this study, a new approach to edge detection on images, corrupted with Gaussian and impulsive noise, is presented. The concept, under the decomposition of image with its principal component being an analysis on local pixel grouping for noise suppression, called LPGPCA based denoising, is adopted in order to obtain noiseless gradient maps for edge extraction. As a result, an algorithm has been developed called LPGPCA-ED. Firstly, horizontal and vertical gradient images are computed; then the gradient images are decomposed into a noiseless phase by applying the LPGPCA algorithm. Once a single gradient map has been obtained, a smart nonmaximum suppression operation is carried out to obtain a binary edge map. To show the accuracy of the proposed edge detector objectively, F-measure and PFOM results of the proposed edge detector on images with different Gaussian and impulsive noise are compared with the results of traditional and certain recently published edge detectors. Objectively, the experimental results on RUG and receiver operating characteristic (ROC) curve databases show that our method has better performance than other corresponding edge detectors. Moreover, subjective experiments on a variety of noise contaminated images show that the LPGPCA-ED algorithm is more robust under high level noise conditions, and is able to reveal well-linked lines and also preserve the structural form of a processed image.

Low level image processing and analysis using radius filters

A novel class of nonlinear L-filters based on local statistics is introduced, which will be called Radius Filters (RFs). The underlying idea for the construction of RFs is the sorting of the values of the input signal belonging in a sliding window, according to their distance from their mean value, in a vector Tr that carries significant signal information, while the filter output is defined as a linear combination of the elements of the vector Tr. RFs are simple, intuitive and easy to implement while they offer a unique signal formation, which provides insight and in depth signal interpretation for the proper filter design. Moreover, they may considered as a generalization of the OS Filters, since OS filters may be easily produced from the RFs. A number of low level image processing applications of the RFs such as impulse noise removal, speckle noise removal, adapting filtering, edge detection in noisy images, image sharpening and image frequency decomposition are outlined.

Comparison of Traditional Approach for Edge Detection with Soft Computing Approach

Image processing supports applications in different fields such as medicine, astronomy, product quality, industrial applications. Edge detection plays important role in segmentation and object identification process. Soft computing approach represents a good mathematical framework to deal with uncertainty of information. The performance of the well-known edge detectors, like Canny, Sobel, etc, depends critically on the choice of the input parameters. Threshold decision is the key uncertainty in the edge detection algorithms. In this paper, an improved edge detection algorithm based on fuzzy combination of mathematical morphology and multiscale wavelet transform is proposed. The proposed method overcomes the limitation of wavelet based edge detection and mathematical morphology based edge detection in noisy images. Method present will give best results for noisy images.

Edge-Detection in Noisy Images Using Independent Component Analysis

Edges in a digital image provide important information about the objects contained within the image since they constitute boundaries between objects in the image. This paper proposes a new approach based on independent component analysis (ICA) for edge-detection in noisy images. The proposed approach works in two phases—the training phase and the edge-detection phase. The training phase is carried out only once to determine parameters for the ICA. Once calculated, these ICA parameters can be employed for edge-detection in any number of noisy images. The edge-detection phase deals with transitioning in and out of ICA domain and recovering the original image from a noisy image. Both gray scale as well as colored images corrupted with Gaussian noise are studied using the proposed approach, and remarkably improved results, compared to the existing edge-detection techniques, are achieved. Performance evaluation of the proposed approach using both subjective as well as objective methods is presented.

Edge detection of noisy images based on cellular neural networks

This paper studies a technique employing both cellular neural networks (CNNs) and linear matrix inequality (LMI) for edge detection of noisy images. Our main work focuses on training templates of noise reduction and edge detection CNNs. Based on the Lyapunov stability theorem, we derive a criterion for global asymptotical stability of a unique equilibrium of the noise reduction CNN. Then we design an approach to train edge detection templates, and this approach can detect the edge precisely and efficiently, i.e., by only one iteration. Finally, we illustrate performance of the proposed methodology from the aspect of peak signal to noise ratio (PSNR) through computer simulations. Moreover, some comparisons are also given to prove that our method outperforms classical operators in gray image edge detection.

Comparison of two-sample tests for edge detection in noisy images

We describe three statistical two-sample tests such as the Wilcoxon test, T-test and Kolmogorov-Smirnov test for detecting edges in noisy images. Each of the edge detectors is based on the computation of a test statistic obtained on the modified grey level values by using an edge height parameter which is compared with the threshold given in existing null distribution tables. Some experimental results for these edge detectors with a Sobel operator on sample images both with and without additive noise are given and the performances of these detectors are compared in terms of the quantitative measures.

Multiresolution gradient-based edge detection in noisy images using wavelet domain filters

We detected edges in noisy images using multiresolution analysis with the wavelet transform. products of wavelet coefficients at several scales were used to identify and locate edges. We found that it was important to consider the changes in edge position at different scales to detect edges in noisy imagery. We analyzed one-dimensional edges and compared the results of our approach with the first derivative of the signal. In addition, we compared the results of noisy images with another wavelet-based edge detection method. Our results led to improved edge detection in noisy images.

A Bayesian approach to edge detection in noisy images

An adaptive method for edge detection in monochromatic unblurred noisy images is proposed. It is based on a linear stochastic signal model derived from a physical image description. The presence of an edge is modeled as a sharp local variation of the gray-level mean value. In any pixel, the statistical model parameters are estimated by means of a Bayesian procedure. Then an hypothesis test, based on the likelihood ratio statistics, is adopted to mark a pixel as an edge point. This technique exploits the estimated local signal characteristics and does not require any overall thresholding procedure.

A simple and sensitive method for directional edge detection in noisy images

A simple solution to the problem of detecting edges between regions in noisy images is presented, which yields a low redundancy high definition weighted edge image, suitable for use as the input to higher pattern recognition algorithms. A detailed algorithm is outlined and its results on several real and synthetic images are presented and compared to the output from a Sobel operator.

Edge detection in noisy images based on the co-occurrence matrix

An edge detection method for noisy images is proposed based on the co-occurrence matrix. In the proposed technique based on the step-edge model, the gray level information is simply converted into a bit-map, i.e. the uniform and boundary regions of an image are transformed into a binary pattern by normalization using the local mean. In this binary bit-map pattern, 0 and 1 are densely distributed near the boundary region while they are randomly distributed in the uniform region. To detect the boundary region, the co-occurrence matrix on the bit-map is introduced. The effectiveness of the proposed technique is shown via a quantitative performance comparison to the conventional edge detection methods and the simulation results for noisy images are also presented.

Towards the unification of band-limited derivative operators for edge detection

Edge detection has been an important subject in image processing. Many linear operators for edge detection in noisy images, including some ‘optimal’ ones, have been proposed. Note that most of them were deduced from the mono-edge model, but in real images there exist always many edge points. So it will be interesting to analyse the problem of multi-edge detection in images. In the present paper, we present at first the optimal filter for mono- and multi-edge detection and show that an optimal filter for noise removing in edge detection is the symmetric exponential filter of an infinite size (ISEF). In more general multi-edge cases, the optimal filter is a linear combination of the output from different stages of a cascade of symmetric exponential filters. Based on the ISEF cascade for multi-edge detection, we analyse then some band-limited linear edge detectors, such as Gaussian filter, Gaussian cascade technique, box technique, fast filter transform, Canny's filter, DRF method, box difference and Gabor's filter used for edge detection. We show that these filters can be explained and unified by the cascade of exponential filters presented for multi-edge detection.

A model-based approach for filtering and edge detection in noisy images

The authors consider the problem of enhancement and edge detection on noisy, real-world images. The restoration and edge detection framework is based on an autoregressive (AR) random-field model. An edge is detected if the first and second directional derivatives and a local estimate of the variance at each point satisfy certain criteria. When noise is present, a good estimate of the original from the noisy images improves the signal-to-noise ratio, resulting in better estimates of the directional derivatives. To avoid excessive computation, the problem of estimation of the original image and the model parameters is presented as a combination of a reduced-update Kalman filter and an adaptive-least-squares parameter estimation algorithm. The restoration process is completed with a min-max replacement scheme to enhance edge strength. An orientation-sensitive detector resulting from the use of an AR model may not detect edges of significantly different orientations. This is partially overcome by running four edge detectors on the four interior pixels of a 4*4 window; this corresponds to rotating the window in successive multiples of 90 degrees . Comparisons with R.M. Haralick's (1984) facet model edge detector, R. Nevatia and K.R. Babu's (1980) line finder, and J. Canny's (1986) edge detector are given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>