Improved self-adaptive wavelet terahertz image denoising method

An adaptive and improved technology, applied in the field of image denoising, can solve the problems of low real-time performance, poor denoising effect, complex algorithm, etc., and achieve the effect of fast and effective denoising and improvement of denoising effect.

Active Publication Date: 2018-06-08
JIMEI UNIV
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Problems solved by technology

[0005] The present invention aims to provide an improved adaptive wavelet terahertz image denoising method to solve the problems of poor denoising effect, complex algorithm and low real-time performance in existing denoising methods

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  • Improved self-adaptive wavelet terahertz image denoising method

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Embodiment Construction

[0029] To further illustrate the various embodiments, the present invention is provided with accompanying drawings. These drawings are a part of the disclosure of the present invention, which are mainly used to illustrate the embodiments, and can be combined with related descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, those skilled in the art should understand other possible implementations and advantages of the present invention. Components in the figures are not drawn to scale, and similar component symbols are generally used to denote similar components.

[0030] The present invention will be further described in conjunction with the accompanying drawings and specific embodiments. figure 1 It is a flow chart of the improved adaptive wavelet terahertz image denoising method of the present invention. The method includes the following steps:

[0031] S1. Input the original terahertz image to be deno...

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Abstract

The invention relates to an improved self-adaptive wavelet terahertz image denoising method. The method comprises the following steps of inputting a to-be-denoised original terahertz image; performingpixel distinguishing on the original terahertz image; performing fuzzy C mean method clustering on pixels, and dividing the original terahertz image into five parts; calculating average texture values of the five parts, and comparing the average texture values of the parts with a set threshold, thereby dividing the five parts into a smooth region and a non-smooth region; selecting proper waveletbasis and decomposition layer numbers for the smooth region and the non-smooth region, and decomposing the layers in sequence by utilizing a decomposition algorithm to obtain wavelet coefficients belonging to the layers; processing the obtained wavelet coefficients, determining a preset threshold according to a threshold selection principle of wavelet transformation, and performing filtering through a self-adaptive threshold function to remove noises; performing image reconstruction by using wavelet inverse transformation; and outputting the whole image. According to the method, the terahertzspectral image can be quickly and effectively denoised.

Description

technical field [0001] The invention relates to the field of image denoising, in particular to an improved adaptive wavelet terahertz image denoising method. Background technique [0002] Terahertz waves cover the frequency range of 0.1THz to 10THz (1THz=10 12 Hz), the frequency band is between infrared and microwave. Before the mid-1980s, due to the lack of efficient emission sources and sensitive detectors in the terahertz band, the electromagnetic radiation in this frequency band has not been deeply studied. The Hertzian band was once called the "terahertz gap" of the electromagnetic spectrum. However, in the past two decades, with the development of material science and laser science, the generation and detection technologies of terahertz waves have developed rapidly, and terahertz technology has gradually been successfully applied to pharmaceuticals, biological detection, Industrial non-destructive testing and national defense security and other fields. [0003] Beca...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06K9/62
CPCG06T2207/20192G06T2207/20064G06T2207/20024G06F18/23213G06T5/70
Inventor 李铁军陈金海梅强黄辉祥黄鹏飞陆欣泽邵桂芳
Owner JIMEI UNIV
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