An analytical method for the adaptive computation of threshold of gradient modulus in 2D anisotropic diffusion filter

A polynomial model for the adaptive computation of threshold of gradient modulus in 2D anisotropic diffusion filter

At present, systems for the recognition (classification) of reflectors based on artificial neural networks are being actively developed to increase the speed of preparation of the ultrasonic testing protocol and reduce the influence of the human factor. For these systems to operate most efficiently, the reflector images must be processed in order to increase the signal-to-noise ratio of the image and its segmentation (clustering). One way of segmentation is to process the image with an adaptive anisotropic diffusion filter, which is used to process optical images. The effectiveness of using this texture filter for segmentation of reflector images reconstructed from echo signals measured with antenna arrays has been demonstrated in model experiments.

Application of Adaptive Anisotropic Diffusion Filter To Improve the Reflector Image Quality when Performing Ultrasonic Nondestructive Testing

The Perona-Malik (PM) model is used successfully in image processing to eliminate noise while preserving edges; however, this model has a major drawback: it tends to make the image look blocky. This work proposes to modify the PM model by introducing the Caputo-Fabrizio fractional gradient inside the diffusivity function. Experiments with natural images show that our model can suppress efficiently the blocky effect. Also, our model has good performance in visual quality, high peak signal-to-noise ratio (PSNR), and lower value of mean absolute error (MAE) and mean square error (MSE).

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Perona-Malik Model with Diffusion Coefficient Depending on Fractional Gradient via Caputo-Fabrizio Derivative

In the field of microbiology, the counting of bacterial colonies is fundamental and mandatory. This is done to estimate the number of bacterial cells in every 1 milliliter or gram of sample. The counting takes a long time and is tedious, so it requires an accurate and fast counting method. The image quality used is very low and contains noise. Therefore, a preprocessing method is needed to reduce the noise. The Perona-Malik filter method is known to be able to remove noise well. However, it is difficult to determine the appropriate gradient threshold parameter ( ) for each different image. To find the appropriate value of , the original Sobel Mask method and Sobel Mask Fractional-Order are used to estimate the value of . The experimental results show the results of noise reduction using PMD with a value of  from the original Sobel Mask and Sobel Mask Fractional-Order. The results of the accuracy of determining the value of k with the Sobel Mask Fractional-Order (α=1.0) show higher results based on the F-Measure values for samples 1, 2, and 3 respectively 97%, 98%, and 90%.

/pdf/counting-bacterial-colony-and-reducing-noise-on-low-quality-2xsco7b6.pdf

Counting Bacterial Colony and Reducing noise on Low-Quality Image Using Modified Perona-Malik Diffusion Filter with Sobel Mask Fractional Order

The aim of this contribution is to make an efficient smoothing algorithm that preserves edges and provides valuable information for any segmentation process. The non-linear anisotropic diffusion (AD) model of Perona–Malik is considered to enhance the edges in the process of diffusion through a variable diffusion coefficient. However, the diffusion coefficient is very sensitive to the so-called contrast or gradient threshold parameter. This article proposes a novel methodology for the estimation of this contrast parameter based on a partition of the image using the K-means algorithm and a least-square fit to approximate the diffusion coefficient (KMLS). The experimental results show that the quality of the edge detection process improves when the proposed algorithm in the smoothing AD is used instead of the traditional techniques. The comparison is performed using two objective edge detection performance measures, the so-called Pratt's figure of merit and the symmetric average distance. Both measures show ...

M. Borroto-Fernández

Papers

New estimation method of the contrast parameter for the Perona–Malik diffusion equation