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Image noise reduction system and method based on K-SVD (Singular Value Decomposition) and locally linear embedding

A local linear nesting and image noise reduction technology, applied in the field of image processing, can solve the problems of reducing image correlation and unfavorable reconstructed image quality

Inactive Publication Date: 2012-11-21
HOHAI UNIV CHANGZHOU
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Problems solved by technology

However, since the image noise reduction criterion based on the K-SVD method is that the reconstructed image obtained by multiplying the dictionary and the sparse signal is close to the noisy image, and the noise has strong randomness, so such a noise reduction criterion must be reduced. The local strong correlation of the image is not conducive to the improvement of the quality of the reconstructed image

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  • Image noise reduction system and method based on K-SVD (Singular Value Decomposition) and locally linear embedding
  • Image noise reduction system and method based on K-SVD (Singular Value Decomposition) and locally linear embedding
  • Image noise reduction system and method based on K-SVD (Singular Value Decomposition) and locally linear embedding

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[0041] The image noise reduction method based on K-SVD and local linear nesting of the present invention will be further elaborated below in conjunction with the accompanying drawings.

[0042] Such as figure 1 , figure 2 As shown, an image denoising system based on K-SVD and local linear nesting includes the following modules: sampling module, calculating Laplacian matrix L module, objective function construction and dictionary, sparse coefficient optimization module, and estimating image block An acquisition module, an overall estimation image block acquisition module;

[0043] Noisy image→sampling module→calculate Laplacian matrix L module→objective function construction and dictionary, sparse coefficient optimization module→estimation image block acquisition module→overall estimated image block acquisition module→denoising image;

[0044] Described objective function construction and dictionary, sparse coefficient optimization module comprise overall objective function ...

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Abstract

The invention discloses an image noise reduction system and method based on K-SVD (Singular Value Decomposition) and locally linear embedding, and particularly relates to a signal sparse representation and reconstruction technique based on dictionary learning and an image noise reduction technique based on manifoid learning. A K-SVD method is adopted to be a frame as the signal sparse representation and reconstruction technique based on the dictionary learning, the locally linear embedding used as a constrain condition is added into a target function while the signal sparse representation is solved, so that the relation of the decomposed sparse coefficients is strengthened, the influence of the random noise on the sparse coefficients is overcome, and the image noise reduction effect better than that of the original K-SVD method is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image noise reduction system and method, in particular to an image noise reduction system and method based on K-SVD and local linear nesting. Background technique [0002] In practical applications, images will inevitably be interfered by various noise signals in the process of acquisition and transmission. Therefore, the noisy image must be processed at the receiving end to improve the signal-to-noise ratio of the image, improve the image quality, and extract true and effective original image information from the noisy image as much as possible. Image noise reduction has always been a hot issue in the field of image processing. Scholars from various countries have also improved the signal-to-noise ratio of images through various signal processing methods. [0003] In recent years, with the deepening of the research on signal processing and reconstruction methods based...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 汤一彬单鸣雷朱昌平韩庆邦高远殷澄
Owner HOHAI UNIV CHANGZHOU
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