An Adaptive Edge Detection Algorithm for Images Corrupted with Impulse Noise (original) (raw)
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A Novel Algorithm for Impulse Noise Removal and Edge Detection
International Journal of …, 2012
Edge detection of images is an important task in computer vision and image processing. Edge detection of noise free images is relatively simpler, but in most practical cases the images are degraded by noise. To find the edges from noisy images is a challenging task. This paper proposes a novel edge detection algorithm for images corrupted with noise. The algorithm finds the edges by eliminating the noise from the image so that the correct edges are determined. For making the image noise free the algorithm calculates closeness parameters, based on this parameter the noisy pixel is replaced by the most appropriate value. The edges of the noise free image are determined using morphological operators erosion and dilation. The proposed algorithm uses a combination of these operators to find the edges. This algorithm uses two different types of structuring elements so that all the edges of the image are determined efficiently.
A Hybrid Method for Edge Detection in Image Corrupted by Impulse Noise
2017
dge detection is a crucial preparatory stage in image processing. The current edge detection methods suffer from the problem of high frailty to noise. In the present paper, a new method for image edge detection in images damaged by Impulse noise introduced. The simple structure of the proposed method is composed of four neural networks, a neuro-fuzzy network and an adaptive median filter. The internal parameters of these networks are adaptively optimized in training through using simple synthetic images that can be generated in a computer. The proposed method is tested on many popular images and the results have been compared with those of the previous edge detectors such as Sobel and Canny. Empirical statistics show that the newly-introduced method presents much better performance than the previous ones and can be benefited in any process of edge detection of the Impulse noise-damaged images.
IEEE Transactions on Image Processing, 2018
This study introduces a robust edge detection method that relies on an integrated process for denoising images in the presence of high impulse noise. This process is shown to be resilient to impulse (or salt and pepper) noise even under high intensity levels. The proposed switching adaptive median and fixed weighted mean filter (SAMFWMF) is shown to yield optimal edge detection and edge detail preservation, an outcome we validate through high correlation, structural similarity index and peak signal to noise ratio measures. For comparative purposes, a comprehensive analysis of other denoising filters is provided based on these various validation metrics. The non-maximum suppression method and new edge following maximum-sequence are two techniques used to track the edges and overcome edge discontinuities and noisy pixels, especially in the presence of highintensity noise levels. After applying predefined thresholds to the grayscale image and thus obtaining a binary image, several morphological operations are used to remove the unwanted edges and noisy pixels, perform edge thinning to ultimately provide the desired edge connectivity which results in an optimal edge detection method. The obtained results are compared to other existing state-of-the-art denoising filters and other edge detection methods in support of our assertion that the proposed method is resilient to impulse noise even under high-intensity levels.
Survey on Various Edge Detection Techniques on Noisy Images
2014
An edge is defined as boundaries of objects or sudden change in an image which is not in a continuous form that helps to detect and identify the objects in a given image. Edge detection is considered as an ignition in Computer Vision and Digital Image Processing. So, it is very important to have a clear concept about edge detection algorithms. There are various edge detections techniques like sobel, prewitt, canny and many others but these methods are not robust for noise. This paper introduces a clear-cut explanation of various edge detection algorithms with comparison on image with salt and pepper noise and preprocessed images. On comparing them we can say that image filtered by median filter gives better output than noisy image.
IJERT-Survey on Various Edge Detection Techniques on Noisy Images
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/survey-on-various-edge-detection-techniques-on-noisy-images https://www.ijert.org/research/survey-on-various-edge-detection-techniques-on-noisy-images-IJERTV3IS100561.pdf An edge is defined as boundaries of objects or sudden change in an image which is not in a continuous form that helps to detect and identify the objects in a given image. Edge detection is considered as an ignition in Computer Vision and Digital Image Processing. So, it is very important to have a clear concept about edge detection algorithms. There are various edge detections techniques like sobel, prewitt, canny and many others but these methods are not robust for noise. This paper introduces a clear-cut explanation of various edge detection algorithms with comparison on image with salt and pepper noise and preprocessed images. On comparing them we can say that image filtered by median filter gives better output than noisy image.
THE PERFORMANCE ANALYSIS OF EDGE DETECTION ALGORITHMS FOR IMAGE PROCESSING IN PRESENCE OF NOISE
Edge Detection is one of the important and most frequently used approaches for Image Segmentation in Digital Image processing. Selection of particular algorithm for detecting edges of images in presence of noise is always a challenging task. This paper mainly focuses on brief Study of different edge detection algorithms for images in presence of noise. In this paper we have studied Prewitt, Sobel, Robert, and Canny edge detection algorithms to find the better method in image edge detection process finally by comparing the experimental results the canny edge detection algorithm gives better results.
A Simple Technique Applied to Edge Detection in Digital Images
2004
In this paper, a new technique for edge detection in the presence of impulsive noise is presented. Several masks are designed in an intuitive way and tested for a gray level image. Result shows how the mask can detect edge in an efficient way. The results are also compared with that of the classical mask algorithms and it is seen that edge detection and noise reduction process is well suited since the recovered image is not blurred. The noisy images are presented in two ways. First, smoothing the image by Gaussian convolution then, detecting the edge by the proposed mask.
A Novel Non-Shannon Edge Detection Algorithm for Noisy Images
Edge detection is an important preprocessing step in image analysis. Successful results of image analysis extremely depend on edge detection. Up to now several edge detection methods have been developed such as Prewitt, Sobel, Zerocrossing, Canny, etc. But, they are sensitive to noise. This paper proposes a novel edge detection algorithm for images corrupted with noise. The algorithm finds the edges by eliminating the noise from the image so that the correct edges are determined. The edges of the noise image are determined using non-Shannon measures of entropy. The proposed method is tested under noisy conditions on several images and also compared with conventional edge detectors such as Sobel and Canny edge detector. Experimental results reveal that the proposed method exhibits better performance and may efficiently be used for the detection of edges in images corrupted by Salt-and-Pepper noise. https://sites.google.com/site/ijcsis/
Edge Detection of Noisy Images Using the Intelligent Techniques
Majlesi Journal of Electrical Engineering, 2013
In this paper an approach is presented for edge detection of noisy images that have been degraded by impulsive noise. It uses Fuzzy Inference System (FIS) and Ant Colony Optimization (ACO). Starting with, using the FIS with 12 simple rules is to identify the noisy pixels in order to perform the filtering operation only for the noisy pixels. Probable edge pixels in 4 main directions for filtered image are detected using fuzzy rules and then ACO is applied by assigning a higher pheromone value for the probable edge pixels rather than other pixels so that the ant’s movement toward edge pixels gets faster. Another factor is the influence of the heuristic information in the movement of any ant that is considered to be proportional to local change in intensity of each pixel in order to the possibility of movement of ants increased toward pixels that have more change in their local intensity. Finally, by using an intelligent thresholding technique which is provided by training a neural net...
Edge detection in noisy images with different edge types
IOP Conference Series: Earth and Environmental Science
Edge Detection in an image is a process that produces edges of image objects, the purpose of which is to mark the parts that become detailed images to improve the details of blurry images, which occur due to the effects of the image acquisition process. Edge is defined as a change in the intensity of a large distance. Based on changes in intensity, there are three types of edges in digital images, namely, step edges, ramps edge, and noisy edge. On step edges where the intensity or gray value changes very fast The Gradient Method is able to detect better. On the ramp edge where the gray value slowly changes the Laplace method is able to detect better than the Gradient Method. On a noisy edge, the existence of noise in the image can bring up the other edges around the actual edge and can also shift the actual edge position. In this study, the authors conducted edge detection in the image by means of determining the right edge detection method to detect edges in noisy images. Noise is obtained by generating a Gaussian Noise in the image. The results showed that, without filtering the image, in noisy edge, the LoG operator was able to detect edges and reduce noise better than Canny. However, by selecting the right threshold that matches the σ (standard deviation), Canny also capable to provide good edge detection results.