Image denoising method based on improved wavelet threshold algorithm

Zhu, Guowu; Liu, Bingyou; Yang, Pan; Fan, Xuan

doi:10.1007/s11042-024-18197-w

Multimed Tools Appl

2024

DOI: 10.1007/s11042-024-18197-w

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Image denoising method based on improved wavelet threshold algorithm

Guowu Zhu,

Bingyou Liu,

Pan Yang

et al.

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2024

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Cited by 3 publications

References 17 publications

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The lidar denoising algorithm based on an improved correlation parameter of ensemble empirical mode decomposition

Tan,

Zhang,

Sun

et al. 2024

J. Korean Phys. Soc.

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The lidar denoising algorithm based on an improved correlation parameter of ensemble empirical mode decomposition

Tan,

Zhang,

Sun

et al. 2024

J. Korean Phys. Soc.

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An Offset Parameter Optimization Algorithm for Denoising in Photon Counting Lidar

Tan,

Zhang,

Sun

et al. 2024

Entropy

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In the case of a weak signal from a photon counting lidar and strong noise from the solar background, the signal is completely submerged by noise, potentially resulting in the appearance of multiple peaks in the denoising algorithm of photon counting entropy. Consequently, a clear distinction between the signal and noise may become challenging, leading to significant fluctuation in the ranging error. To solve this problem, this paper proposes an improved offset parameter optimization algorithm under the framework of photon counting entropy, aiming to effectively eliminate peak interference caused by noise and enhancing ranging accuracy. The algorithm includes two aspects. First, we introduce the solar irradiance prediction of an MLP network and least squares linear conversion to accurately estimate the noise rate of the solar background noise. Then, we propose the offset parameter optimization method to effectively mitigate the interference caused by noise. In simulation and experimental analyses, the ranging error of our proposed method is within 5 and 30 cm, respectively. Compared with the denoising method of photon counting entropy, the average ranging error is increased by 81.99% and 73.76%. Furthermore, compared to other anti-noise methods, it exhibits superior ranging capability.

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An efficient method for image denoising based on a new nonlinear wavelet thresholding function

Dehda,

Aoun

2024

SEES

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Image noise is random variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the image sensor and circuitry of a scanner or digital camera. In fact, there are different kinds of noise functions such as Gaussian noise, salt and pepper noise and speckle noise. Hence, image denoising is the process of removing noise from an image using one of the denoising methods such as spatial or transform techniques. The main aim of an image denoising technique is to achieve both noise reduction and feature preservation. In this context, the wavelet denoising method works in the transform domain, where the noise is uniformly spread throughout coefficients while most of the image information is concentrated in a few large ones.Therefore, the first wavelet-based denoising methods were based on thresholding of detail subband coefficients. However, most of the wavelet thresholding methods suffer from the drawback that the chosen threshold may not match the specific distribution of the image and noise components. Another thing: Studies have proven that the thresholding function has an effective role in obtaining better quality of the denoised image. To address these disadvantages, in this paper, we propose an efficient method for image denoising in wavelets domain. This method is based on a new nonlinear wavelet thresholding function, that is characterized by main mathematical properties and a shape parameter. The theoretical study of this new method proves that we can overcome the drawbacks of classical thresholding methods and by freely adjusting the shape parameter, we achieve a compromise between Hard and Soft thresholding. The experimental results show that our proposed method provides better performance compared to classical thresholding methods in terms of the visual quality of the denoised image.

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Image denoising method based on improved wavelet threshold algorithm

Cited by 3 publications

References 17 publications

The lidar denoising algorithm based on an improved correlation parameter of ensemble empirical mode decomposition

The lidar denoising algorithm based on an improved correlation parameter of ensemble empirical mode decomposition

An Offset Parameter Optimization Algorithm for Denoising in Photon Counting Lidar

An efficient method for image denoising based on a new nonlinear wavelet thresholding function

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