Weighted sparse-based mixed denoising method

Abstract

The invention discloses a weighted sparse-based mixed denoising method. According to the method, a calculus of variations is added on the basis of a weighted sparse representation non-local training dictionary to match non-local similar blocks, and a dual method is used to solve a mixed denoised image, so that edge information of the image can be better stored. The method has a denoising effect superior to that of the existing algorithms, has a high peak signal to noise ratio and a high image feature similarity, has a good inhibiting effect for mixed noises, particularly can well store the image information of the image, and has certain improvement for the preservation of image features.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to a method for removing mixed noise based on weighted sparseness. Background technique [0002] The image will inevitably be polluted by noise during the process of acquisition and transportation, and the image polluted by noise will affect the subsequent processing of the image, such as edge detection, target recognition and image segmentation. Therefore, denoising is an important issue in image digital image processing. Its main purpose is to restore an ideal image from a given noisy image while preserving the details, textures, and edges of the image. Since the mixed noise has no parameter model and its distribution is complex, it is quite difficult to effectively remove the mixed noise. [0003] In recent years, people have long been committed to the improvement of mixed noise removal methods, such as Xiao et al. 1 -l 0 minimization, "Pattern Rec...

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Problems solved by technology

Since the mixed noise has no parameter model and the distribution is complex, it is quite difficult to effectively remove the mixed noise

[0003] In recent years, people have long been committed to the improvement of mixed noise removal methods, such as Xiao et al. 1 -l 0 minimization, "Pattern Recognit., vol.44, no.8, pp.1708-1720, Aug, 2010) first use the median filter for impulse noise detection, and finally use l 1 -l 0 The minimum optimization problem solves the denoising image. Although this method improves the visual quality of the denoising image, it is computationally intensive

In 2014, Jiang et al. (J. Jiang, L. Zhang, and J. Yang, "Mixed noise removal by weighted encoding with sparse nonlocal regularization," IEEE Trans. Image Process., vol.23, no.6, pp.2651-2262, 2014) (JZY model) combined with the method of non-local sparse representation, although the algorithm can remove Gaussian white noise and impulse noise at the same time and does not need to detect the impulse noise first, but this method makes the edges of the denoising image blurred

Meanwhile Zhang et al. (J. Zhang, D.B. Zhao, and W. Gao, "Group-based sparse representation for image restoration," IEEE Trans. Image Process., vol.23, no.8, pp.3336-3351, 2014) (ZZG model) proposed a method based on group sparse representation to remove mixed noise, but the denoised image obtained by this method is too smooth

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