A study on the effect of wavelet basis function selection on image denoising

Tianqi Han; Jingjing Fan; Jianguang Zhao; Sijia Yin

doi:10.1117/12.2631824

14 April 2022 A study on the effect of wavelet basis function selection on image denoising

Tianqi Han, Jingjing Fan, Jianguang Zhao, Sijia Yin

Proceedings Volume 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021); 121780T (2022) https://doi.org/10.1117/12.2631824
Event: International Conference on Signal Processing and Communication Technology (SPCT 2021), 2021, Tianjin, China

Abstract

The contamination caused by noise to the image leads to the image missing key information or reflect the role of the real situation. Wavelet denoising based on the spatial domain has a certain denoising effect on the noise. When doing wavelet decomposition on the image, because the wavelet function has the feature of flexibility in base selection, different choices of wavelet basis functions have different denoising effects on the noisy image. Simulation experiments are conducted by Python for several commonly used wavelet basis functions using the control variables method. The final experimental results are evaluated by PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity).

Citation Download Citation

Tianqi Han, Jingjing Fan, Jianguang Zhao, and Sijia Yin "A study on the effect of wavelet basis function selection on image denoising", Proc. SPIE 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021), 121780T (14 April 2022); https://doi.org/10.1117/12.2631824

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