Image noise reduction method

Abstract

The invention discloses an image noise reduction method, overcoming the problem of fixed bias between a wavelet coefficient obtained by an image noise reduction method in the existing image processing technology and a wavelet coefficient of an original image. The image noise reduction method comprises the following steps of carrying out multilevel wavelet decomposition on to-be-processed noise images, thereby acquiring the corresponding multilevel wavelet coefficients; according to the wavelet coefficients at all levels and the corresponding level numbers of the wavelet coefficients, determining the corresponding noise thresholds of the wavelet coefficients at all levels; carrying out noise reduction on the multilevel wavelet coefficients by adopting a corresponding wavelet threshold noise reduction function of multiple noise thresholds based on the multilevel wavelet coefficients; and reconstructing the original images corresponding to the noise images by adopting the noise-reduced multilevel wavelet coefficients. The constant error among the wavelet coefficients obtained by the image noise reduction method and the wavelet coefficients of the original images is relatively small so that pseudo-Gibbs artifacts can be avoided, detailed information of the images are well retained, and the calculated quantity is relatively low. Thus, the image noise reduction method can be widely applied to the wireless broadcasting field.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image denoising method. Background technique [0002] Image denoising is the process of reducing noise in digital images. Commonly used image denoising methods can be divided into spatial domain algorithms and temporal domain algorithms. The spatial domain algorithm is to directly process the image pixels, and the frequency domain algorithm is to transform the image from the time domain to the frequency domain, process the image in the frequency domain, and then transform the processed image to the time domain. The frequency domain algorithm is more widely used because of its excellent performance. In the frequency domain algorithm, the wavelet threshold algorithm has become a commonly used frequency domain method because of its simple denoising method and good effect. Currently popular wavelet threshold algorithms include soft threshold denoising algorithm and hard threshold...

AI Technical Summary

Problems solved by technology

[0004] Although the denoising method of the hard threshold algorithm is simple and the calculation speed is fast, because the denoising function of the hard threshold is discontinuous at the threshold, the image will appear pseudo-Gibbs phenomenon and cause visual distortion. Therefore, D.L.Donoho et al. proposed a soft threshold The algorithm improves the hard threshold algorithm

However, the soft threshold algorithm also has problems such as fixed deviation generated during threshold processing, poor retention of image detail information after denoising, and relatively large amount of calculation.

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