Algorithm for Infrared Image Noise Filtering Based on Anisotropic Diffusion

Junqi, 白俊奇 Bai; Qian, 陈钱 Chen

doi:10.3788/aos20082805.0866

Cited by 3 publications

(3 citation statements)

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“…Here, rði; jÞ denotes the local standard deviation of the window M Â N. It not only can reasonably reflect the gradient difference between the boundary and the smooth region but also it can better reduce the sensitivity to noise, because it correlates with multi-pixel points in the sliding window. 28 Its expression is…”

Section: R E T R a C T E Dmentioning

confidence: 99%

RETRACTED: A compensating total variation image denoising model combining L₁ and L₂ norm

Zhang

Qiu

2020

International Journal of Electrical Engineering & Education

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To overcome the “staircase effect” while preserving the structural information such as image edges and textures quickly and effectively, we propose a compensating total variation image denoising model combining L1 and L2 norm. A new compensating regular term is designed, which can perform anisotropic and isotropic diffusion in image denoising, thus making up for insufficient diffusion in the total variation model. The algorithm first uses local standard deviation to distinguish neighborhood types. Then, the anisotropic diffusion based on L1 norm plays the role of edge protection in the strong edge region. The anisotropic and the isotropic diffusion simultaneously exist in the smooth region, so that the weak textures can be protected while overcoming the “staircase effect” effectively. The simulation experiments show that this method can effectively improve the peak signal-to-noise ratio and obtain the higher structural similarity index and the shorter running time.

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Section: R E T R a C T E Dmentioning

confidence: 99%

RETRACTED: A compensating total variation image denoising model combining L₁ and L₂ norm

Zhang

Qiu

2020

International Journal of Electrical Engineering & Education

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“…How to better preserve the edge information and structural information of images while maintaining effective denoising is a hot issue in image denoising. Gaussian filtering algorithm [1], bilateral filtering algorithm [2], anisotropic diffusion algorithm [3], and median filtering algorithm [4] are commonly used noise denoising methods. These classical algorithms have achieved a certain degree.…”

Section: Introductionmentioning

confidence: 99%

Overview of image noise reduction based on non-local mean algorithm

Liu

2018

MATEC Web Conf.

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The system introduces the extensive application and development process of image denoising based on non-local mean. The principle and specific theoretical model of the non-local mean algorithm are described. The process of improving the non-local mean algorithm after being proposed and how to improve it is elaborated and the shortcomings of the algorithm are pointed out. The noise reduction algorithm is experimentally described in detail from the aspects of peak signal-to-noise ratio, mean square error and structural similarity under different noise environment conditions.

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“…How to retain the edge information and structure information of an image while maintaining effective denoising is a problem in image denoising research. Gaussian filtering algorithm [2], bilateral filtering algorithm [3], anisotropic diffusion algorithm [4], and median filtering algorithm [5] are commonly used noise reduction methods. The denoising effect is achieved to a certain extent but there are also disadvantages that the image structure information cannot be preserved well.…”

Section: Introductionmentioning

confidence: 99%

An Improved Non-local Mean Ultrasound Image Denoising Algorithm

Liu¹,

Liu²

2018

Proceedings of the 2018 3rd International Conference on Control, Automation and Artificial Intelligence (CAAI 2018)

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An improved non-local mean ultrasound image denoising method is proposed to solve the problem of poor effect of traditional image denoising algorithm on ultrasound images. In this method the non-local mean filtering algorithm is improved by using the gamma distribution noise model and image block matching, so as to get better noise reduction effect. By comparing with Gaussian filtering algorithm, median filtering algorithm and bilateral filtering algorithm, the improved algorithm achieves better noise reduction than traditional methods, and compared with early non-local mean algorithm in peak signal-to-noise ratio and mean square error performance were optimized.

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Algorithm for Infrared Image Noise Filtering Based on Anisotropic Diffusion

Cited by 3 publications

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RETRACTED: A compensating total variation image denoising model combining L₁ and L₂ norm

RETRACTED: A compensating total variation image denoising model combining L₁ and L₂ norm

Overview of image noise reduction based on non-local mean algorithm

An Improved Non-local Mean Ultrasound Image Denoising Algorithm

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Algorithm for Infrared Image Noise Filtering Based on Anisotropic Diffusion

Cited by 3 publications

References 0 publications

RETRACTED: A compensating total variation image denoising model combining L1 and L2 norm

RETRACTED: A compensating total variation image denoising model combining L1 and L2 norm

Overview of image noise reduction based on non-local mean algorithm

An Improved Non-local Mean Ultrasound Image Denoising Algorithm

Contact Info

Product

Resources

About

RETRACTED: A compensating total variation image denoising model combining L₁ and L₂ norm

RETRACTED: A compensating total variation image denoising model combining L₁ and L₂ norm