A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

Wang, Gang; Wang, Zesong; Liu, Jinhai

doi:10.1155/2017/4065306

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…Isolated burrs in the image can be filtered out by equation ( 5) because an important feature of open operation is that it can effectively suppress the positive peak noise with small structural elements. Wang et al [15] explained the basic operation of morphology; while keeping the size of the Mathematical Problems in Engineering target unchanged, the open operation can remain roughly the same and play a smoothing role. However, if the distance between the structural elements is far greater than the noise points, the image quality after operation will be very poor.…”

Section: ① Dilation Operationmentioning

confidence: 99%

Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

Wang

et al. 2021

Mathematical Problems in Engineering

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The traditional canny edge detection algorithm has its limitations in the aspect of antinoise interference, and it is susceptible to factors such as light. To solve these defects, the Canny algorithm based on morphological improvement was proposed and applied to the detection of agricultural products. First, the algorithm uses the open and close operation of morphology to form a morphological filter instead of the Gaussian filter, which can remove the image noise and strengthen the protection of image edge. Second, the traditional Canny operator is improved to increase the horizontal and vertical templates to 45° and 135° to improve the edge positioning of the image. Finally, the adaptive threshold segmentation method is used for rough segmentation, and on this basis, double detection thresholds are used for further segmentation to obtain the final edge points. The experimental results show that compared with the traditional algorithm applied to the edge detection of agricultural products, this algorithm can effectively avoid the false contour caused by illumination and other factors and effectively improve the antinoise interference while more accurate and fine detection of the edge of real agricultural products.

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Section: ① Dilation Operationmentioning

confidence: 99%

Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

Wang

et al. 2021

Mathematical Problems in Engineering

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“…While wavelet transform domain is widely used in the signal processing technique, as well as being a superior method for improving image quality because of its multiresolution and subbands properties, in addition to its ability to represent signals in both domains (time and frequency), as is well-known [11]. Donoho and Johnstone proposed wavelet thresholding as a technique for estimating and calculating the value of the signal by employing wavelet transformation capabilities to destroy non-significant coefficients relative to a certain threshold and re-construction the basis signal characteristics based on thresholding coefficients (coefficients subbands) to reduce noise significantly.…”

Section: Introductionmentioning

confidence: 99%

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Ismael¹,

Baykara²

2021

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Recently, with the explosion in the number of digital images captured every day in all life aspects, there is a growing demand for more detailed and visually attractive images. However, the images taken by current sensors are inevitably degraded by noise in various fields, such as medical, astrophysics, weather forecasting, etc., which contributes to impaired visual image quality. Therefore, work is needed to reduce noise by preserving the textural, information, and structural features of the image. So far, there are different techniques for reducing noise that various researchers have done. Each technique has its advantages and disadvantages. In this paper, a review of some significant work in the field of image denoising based on that the denoising methods is presented. These methods can be classified as wavelet domain, spatial domain, or both methods can combine to obtain the advantage them. After a brief discussion, the classification of image denoising techniques is explained. A comparative analysis of various image denoising methods is also performed to help researchers in the image denoising area. Besides, standard measurement parameters have been used to compute the results and to evaluate the performance of the used denoising techniques. This review paper aims to provide functional knowledge of image denoising methods in a nutshell for applications using images to provide ease for selecting the ideal strategy according to the necessity.

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“…Due to this, it is difficult to differentiate the true and false edges based on the differential edge detector. Multiscale edge detection has attracted a lot of attention in image processing applications due to the fact that edges are multiscale in nature [6]- [8]. The edges extracted from a different scale or resolution are then combined to get the final edge detection result.…”

Section: Introductionmentioning

confidence: 99%

Comparative studies of multiscale edge detection using different edge detectors for MRI thigh

2021

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Edge detection plays an important role in computer vision to extract object boundary. Multiscale edge detection method provides a variety of image features by different resolution at multiscale of edges. The method extracts coarse and fine structure edges simultaneously in an image. Due to this, the multiscale method enables more reliable edges are detected. Most of the multiscale methods are not translation invariant due to the decimated process. They mostly depend on the corresponding transform coefficients. These methods need more computation and a larger storage space. This study proposes a multiscale method that uses an average filter to smooth image at three different scales. Three different classical edge detectors namely Prewitt, Sobel and Laplacian were used to extract the edges from the smooth images. The edges extracted from the different scales of smooth images were then combined to form the multiscale edge detection. The performances of the multiscale images extracted from the three classical edge detectors were then compared and discussed.

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A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

Cited by 18 publications

References 29 publications

Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Comparative studies of multiscale edge detection using different edge detectors for MRI thigh

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