An hybrid denoising algorithm based on directional wavelet packets

Averbuch, Amir; Neittaanmäki, Pekka; Zheludev, Valery A.; Salhov, Moshe; Hauser, Jonathan

doi:10.1007/s11045-022-00836-w

Cited by 4 publications

(1 citation statement)

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“…Such a decomposition means that some high-frequency information of an image will be lost while filtering the noise. In recent years, many scholars have studied image processing techniques based on wavelet packet decomposition [35,36,41]. Wavelet packet analysis is developed based on wavelet analysis, and it provides a more refined frequency analysis method for signals, decomposing not only the scale space but also the wavelet space, and obtains both the detail coefficients and approximation coefficients at the same time.…”

Section: Wavelet Packet Threshold Methodsmentioning

confidence: 99%

An image zooming method based on the coupling threshold in the wavelet packet transform domain

Liu,

Wang

2023

Meas. Sci. Technol.

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Aiming at the problem of blurring edge and detail information in the process of image zooming, this paper proposes a new method of image zooming based on wavelet packet transform by combining the characteristics of anisotropic diffusion. First, the initial zoomed image with higher resolution is obtained by wavelet transform, and the wavelet packet decomposition is performed to obtain more high-frequency wavelet packet coefficients reflecting image details. Second, due to the existence of noise, the relationship between wavelet packet transform and anisotropic diffusion is obtained by studying the process of wavelet packet threshold denoising, and the expression of coupling threshold based on diffusion function is given and applied to the high-frequency wavelet packet coefficients. Finally, the original image is used as the low-frequency part after passing the soft threshold, and reconstructed with the denoised high-frequency part to obtain the final zoomed image. The traditional zooming algorithms as well as the learning-based zooming algorithms are selected for comparison. The results show that the algorithm in this paper effectively avoids the blurring of edges and details under the premise of ensuring the similarity between the zoomed image and the original image, so that the zoomed image can obtain more high-frequency information, and achieves the purpose of removing the noise as well as enhancing the detailed information of the image. The effectiveness of this paper’s algorithm in edge protection can be seen from the results of the comparison with the deep learning-based zooming algorithms.

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Section: Wavelet Packet Threshold Methodsmentioning

confidence: 99%