<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper first classifies the image noise and evaluates the image quality by means of the correlation function, mean square error value, and fidelity. Secondly, an image adaptive threshold denoising system based on wavelet transform is constructed, and the image processing is realized by using the wavelet transform principle and the selection of threshold value. Finally, the image is optimized by using the modulo-maximum denoising method and the threshold denoising method for empirical analysis. The results show that the signal-to-noise ratio of the noisy signal is 6.2315dB, and the signal-to-noise ratio of the modulo-maximum processing is 12.7024 dB. The peak signal-to-noise ratio of the noisy image is 20.1258dB, the peak signal-to-noise ratio of the soft threshold denoising method is 26.4831dB, and the peak signal-to-noise ratio of the hard threshold denoising method is 22.5864dB. This shows that the wavelet transform and adaptive thresholding can effectively denoise and ensure image quality. Image quality.</p>
</abstract>

This paper first classifies the image noise and evaluates the image quality by means of the correlation function, mean square error value, and fidelity. Secondly, an image adaptive threshold denoising system based on wavelet transform is constructed, and the image processing is realized by using the wavelet transform principle and the selection of threshold value. Finally, the image is optimized by using the modulo-maximum denoising method and the threshold denoising method for empirical analysis. The results show that the signal-to-noise ratio of the noisy signal is 6.2315dB, and the signal-to-noise ratio of the modulo-maximum processing is 12.7024 dB. The peak signal-to-noise ratio of the noisy image is 20.1258dB, the peak signal-to-noise ratio of the soft threshold denoising method is 26.4831dB, and the peak signal-to-noise ratio of the hard threshold denoising method is 22.5864dB. This shows that the wavelet transform and adaptive thresholding can effectively denoise and ensure image quality. Image quality.

Optimization of image processing methods based on wavelet transform and adaptive thresholding

Department of Electronic and Information Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper first classifies the image noise and evaluates the image quality by means of the correlation function, mean square error value, and fidelity....

Optimization of image processing methods based on wavelet transform and adaptive thresholding

Data publikacji: 17 paź 2023

Zakres stron: -

Otrzymano: 10 lis 2022

Przyjęty: 27 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00665

Słowa kluczowe
Image noise, Image quality, Wavelet transform, Adaptive thresholding, Threshold denoising method

© 2023 Xinrui Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Optimization of image processing methods based on wavelet transform and adaptive thresholding

Data publikacji: 17 paź 2023

Zakres stron: -

Otrzymano: 10 lis 2022

Przyjęty: 27 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00665

Słowa kluczoweImage noise, Image quality, Wavelet transform, Adaptive thresholding, Threshold denoising method

© 2023 Xinrui Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Image noise, Image quality, Wavelet transform, Adaptive thresholding, Threshold denoising method