Design of an adaptive fractional order image denoising model based on image complexity

Mingxia Liu; Liming Tang; Zhuang Fang; Yanjun Ren

doi:10.1117/12.2674262

28 April 2023 Design of an adaptive fractional order image denoising model based on image complexity

Mingxia Liu, Liming Tang, Zhuang Fang, Yanjun Ren

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 1262606 (2023) https://doi.org/10.1117/12.2674262
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

To determine the order of fractional regularization and achieve a good denoising effect, we described the image complexity from three aspects: the appearance of the image gray level, the spatial distribution of the gray level, and the appearance of the target object. It is composed of five factors, respectively the entropy of information, energy, contrast, pertinence and edge ratio. Then the automatic selection of order is realized, and the alternating direction multiplier method is used to solve the model. The experiment result shows that the improved model not only achieves the self-adaptability of the order but also has a good effect in removing noise and preserving texture.

Citation Download Citation

Mingxia Liu, Liming Tang, Zhuang Fang, and Yanjun Ren "Design of an adaptive fractional order image denoising model based on image complexity", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 1262606 (28 April 2023); https://doi.org/10.1117/12.2674262

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KEYWORDS

Image denoising

Denoising

Cooccurrence matrices

Fourier transforms

Image processing

Signal to noise ratio

Design and modelling

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