Towards a partial differential equation remote sensing image method based on adaptive degradation diffusion parameter

Meng, Xianyong; Che, Lei; Liu, Zhihui; Che, Ning; Ji, Xiaonan

doi:10.1007/s11042-015-2881-1

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…However, there are still loopholes in this diffusion equation [2], whose uniform diffusion makes it impossible to retain important details such as edges while removing noise. In view of the defects of the uniform diffusion characteristics of Gaussian filtering, it is natural to come up with an ideal method: reduce the diffusion at the edge of the image according to the prior information of the image, so as to remove the noise from the image and protect the information at the edge [3]. erefore, we use the gradient operator as the operator of the edge detection to construct a monotone decreasing function [4] in which the diffusion coefficient changes with the gradient of the original image; that is, the magnitude of the gradient is inversely proportional to the diffusion coefficient.…”

Section: Related Workmentioning

confidence: 99%

“…However, the edge protection ability of the model is not good, and it is easy to produce speckle noise. In [3], an improved fourth-order diffusion equation model is proposed, which uses the modulus of a gradient to replace the absolute Laplace value as the operator of image edge detection. is model has a faster convergence speed and better denoising effect but blurred image edges.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning

Shaobin

Lan

Zhang

2022

Journal of Robotics

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Effective noise removal has become a hot topic in image denoising research while preserving important details of an image. An adaptive threshold image denoising algorithm based on fitting diffusion is proposed. Firstly, the diffusion coefficient in the diffusion equation is improved, and the fitting diffusion coefficient is established to overcome the defects of texture detail loss and edge degradation caused by excessive diffusion intensity. Then, the threshold function is adaptively designed and improved so that it can automatically control the threshold of the function according to the maximum gray value of the image and the number of iterations, so as to further preserve the important details of the image such as edge and texture. A neural network is used to realize image denoising because of its good learning ability of image statistical characteristics, mainly by the diffusion equation and deep learning (CNN) algorithm as the foundation, focus on the effects of activation function of network optimization, using multiple feature extraction technology in-depth networks to study the characteristics of the input image richer, and how to better use the adaptive algorithm on the depth of diffusion equation and optimization backpropagation learning. The training speed of the model is accelerated and the convergence of the algorithm is improved. Combined with batch standardization and residual learning technology, the image denoising network model based on deep residual learning of the convolutional network is designed with better denoising performance. Finally, the algorithm is compared with other excellent denoising algorithms. From the comparison results, it can be seen that the improved denoising algorithm in this paper can also improve the detail restoration of denoised images without losing the sharpness. Moreover, it has better PSNR than other excellent denoising algorithms at different noise standard deviations. The PSNR of the new algorithm is greatly improved compared with the classical algorithm, which can effectively suppress the noise and protect the image edge and detail information.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning

Shaobin

Lan

Zhang

2022

Journal of Robotics

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“…7-8) determines the number of elements in the line of the original bitmap (Eq. 12): m=Nj-1 (12) Since the interpixel distance in the line of the original bitmap db is a constant value, which is calculated by (5), the distance along the surface to each distance element can be calculated as (Eqs. 13-14):…”

Section: X=nx--k 1 (1)mentioning

confidence: 99%

“…It is also influenced by saturation of soils during water seepage through the bottom and banks of canals, leaks from water supply and sewerage networks [4]. According to various estimates, flooding affects up to 30% of the population, 60% of industrial-urban agglomerations, almost all developed mining areas and industrial sites of thermal power plants [5].…”

Section: Introductionmentioning

confidence: 99%

The use of radar technologies in the hydraulic engineering in seismic zones

Oralbekova¹,

Tyulepberdinova²,

Gaziz³

et al. 2021

J Appl Eng Science

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Hydraulic structures are designed in a standard way if it is necessary to regulate the water course and maintain the volume of irrigation. The obstacles to the construction of such structures are mainly technical and economic, when instead of building a new hydraulic complex, it is gradually modernised. Consideration of the issues of creating complexes of hydraulic structures leads to the statement of a fact that the water balance of the territory changes. The novelty of the study is determined by the fact that hydraulic structures can be used as prerequisites for the development of qualitatively new programmes for runoff regulation. The authors note that this is particularly important for seismologically unstable areas. It is necessary to lay out plans taking into account not only the possible seismological load, but also the modes and technologies that are used to modernise the already operating hydraulic structures. The practical significance of the study is determined by the fact that the development of hydrotechnical complexes makes it possible to create a system not only for improving the quality of the water balance, but also performs technological support for the safety of the technologies used.

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