Edge detection for phytoplankton cellular based on multi-wavelets de-noising

Sang, Xueqin; Li, Minglong; Ji, Guangrong; Wang, Nengqiang

doi:10.1109/iccae.2010.5451453

Cited by 1 publication

(2 citation statements)

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“…Most edge detector programs determine the color or the intensity values of the edge pixels. In images without noise, edges are recognized by the gray level changing at a specific pixel [8]. The higher is the gray level changing, the easier is the detection.…”

Section: Introductionmentioning

confidence: 99%

“…The random gray level changes of the pixels appear as some discontinuities in the detected edges. Hence, edge detection in noisy images is a nontrivial problem due to the random damage in the image [8]. To reduce noise effects, Gaussians filtering [10], and some algorithms based on Wavelet Transform [11], mathematical morphology [12], neural networks [13], fuzzy networks [14,15] and an enhanced median filter [16] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Noisy images edge detection: Ant colony optimization algorithm

Dorrani

Mahmoodi

2016

JAIDM

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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.

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Section: Introductionmentioning

confidence: 99%