Image de-noising method based on sparse self-adapted dictionary

Abstract

The invention discloses an image de-noising method based on a sparse self-adapted dictionary, and the method is mainly used for overcoming the defects that over-fitting exists when an existing method is used for training the dictionary, and the self-adaption is insufficient. The realization process comprises the following steps of: (1) obtaining image blocks from images with noises and paralleling the image blocks into vectors to form a training data set; (2) utilizing the training data set to iteratively train the dictionary; in an iteration process, taking the dictionary obtained by iteration as a basic dictionary of the iteration, and after the iteration is finished, obtaining a final dictionary and an encoding coefficient matrix of the training data set on the dictionary; (3) utilizing the dictionary and the encoding coefficient matrix obtained by training to obtain a de-noised data set; and (4) utilizing the de-noised data set to reconstruct a de-noised image. The dictionary trained by the method disclosed by the invention has sparseness and better self-adaptation; the effect of de-noising the image is improved; and the method can be used for de-noising a natural image and a medical CT (Computed Tomography) image.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image denoising method containing additive noise, which can be used for denoising natural images and medical CT images. Background technique [0002] Image denoising is a very important issue in the field of image processing. In the process of image acquisition, due to the problems of the equipment itself and the interference in the transmission process, the acquired image inevitably contains noise, which reduces the quality of the image and affects the subsequent processing. Therefore, image denoising is necessary and very meaningful. In practice, most noise can be approximated as additive Gaussian white noise, so removing Gaussian white noise in noisy images has become a very important direction in the field of image denoising. [0003] Traditional denoising methods can generally be divided into two categories, one is based on the spatial domain, and the other is ...

AI Technical Summary

Problems solved by technology

Because the dictionary is trained on a dataset composed of noisy image blocks, this makes the trained dictionary noisy, which affects the denoising effect

At the same time, the dictionary learning speed in this method is slow, and there is over-learning when the training data set is small.

Although the S-KSVD algorithm proposed on the basis of the KSVD algorithm improves the speed and alleviates the shortcomings of KSVD over-learning, it has the disadvantage of relatively poor denoising effect.

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