Single Image Based Random-Value Impulse Noise Level Estimation Algorithm

Bao, Long; Panetta, Karen; Agaian, Sos С.

doi:10.1007/978-3-030-01177-2_95

Cited by 1 publication

(1 citation statement)

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“…If not, it is kept. Subsequently, many scholars proposed various improved algorithms based on this principle [8,9]. Shrestha proposed the progressive switching filter [10], which makes the image locally smooth.…”

Section: Introductionmentioning

confidence: 99%

A Digital Denoising Method Based on Data Frequency Statistical Filtering

Luo

Yuan

et al. 2022

Applied Sciences

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Noise amplitude in original time domain data is usually discrete and sparse. This article presents a digital filter denoising method based on statistical frequencies of the signal values. The effective signal and noise signal are identified by comparing the frequency of the value of each pixelin the original signal with the preset validity discrimination threshold. Signals recognized as valid will be output directly, while noise signals will be replaced by the mean value of their surrounding pixel values. Compared to classical digital filtering methods such as mean filtering and median filtering, this method may improve signal recognition accuracy and has the potential to remove random noise while retaining details. An image noise reduction software based on frequency statistics was developed in the MATLAB environment. Noise reduction based on this algorithm was implemented on a portrait image with a noise density of 5%~40%, and noise reduction efficiency was compared to the classical noise reduction algorithms. The experimental results show that the PSNR of the proposed new method exceeds 41, reaching the same level as switching median filtering and adaptive filtering and preceding mean filtering. The SSIM of the new method exceeds 0.97, which is better than other classical methods. Additionally, the higher the noise density, the more obvious the advantage of this method.

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Section: Introductionmentioning

confidence: 99%