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Image denoising method based on Shearlet contraction and improved TV model

An image and model technology, applied in the field of image processing, can solve the problems of not being able to obtain better noise suppression and edge preservation effects at the same time, and achieve the effects of suppressing pseudo-Gibbs oscillation, low computational complexity, and noise removal

Active Publication Date: 2012-09-12
西安谦腾进科技有限公司
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Problems solved by technology

[0008] In order to avoid the deficiencies of the prior art, the present invention proposes an image denoising method based on Shearlet contraction and improved TV model, which overcomes the disadvantages that the prior art methods cannot achieve better noise suppression and edge preservation effects at the same time

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  • Image denoising method based on Shearlet contraction and improved TV model
  • Image denoising method based on Shearlet contraction and improved TV model
  • Image denoising method based on Shearlet contraction and improved TV model

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Embodiment Construction

[0026] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0027] Step 1: For the original noisy image u 0 Carry out the Shearlet transform decomposition to obtain the high-frequency coefficient C of each scale H and low frequency coefficient C L , and divide the high frequency subbands.

[0028] Step 2: Use the Monte-Carlo method to estimate the noise variance for each scale subband, and then estimate the high frequency coefficient C of each scale H Perform hard thresholding to obtain the denoised high-frequency coefficient C H '.

[0029] Step 3: The high frequency coefficient C obtained in step 2 H ’ and the low-frequency coefficient C obtained in step 1 L Perform the Shearlet inverse transform to obtain the reconstructed image, and obtain the image u after the initial denoising 1 .

[0030] Step 4: Combining the improved total variation model for the initial denoising image u 1 Perform secondary denoising to...

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Abstract

The invention relates to an image denoising method based on a Shearlet contraction and an improved TV model, wherein a TV-denoising model is improved and a novel mixed denoising method by combining the Shearlet contraction is proposed. The method organically combines the sparse representation capability of Shearlet for a high dimension function with the protection capability of the TV-denoising model for an edge, wherein the method obtains a first-denoising image through a hard threshold function contraction, and then improves fidelity terms of a total variation model, and then makes a second denoising of the false Gibbs effect of the first-denoising image by combing the improved total variation model. On the prerequisite of protecting important information such as edges, etc., the method effectively inhibits the false Gibbs oscillation caused by the Shearlet contraction, and realizes a better visual effect and a lower computation complexity.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to an image denoising method based on Shearlet contraction and improved TV model. Background technique [0002] Image denoising is a basic and extensive research topic in the field of computer vision and image processing. The key to denoising is to effectively suppress noise while maintaining important details in the image, such as textures and edges. Among various image denoising methods, the multi-scale geometric analysis method developed from wavelet theory and the denoising method based on partial differential equations have outstanding performance in the application of image denoising. [0003] At present, with the development of computational harmonic analysis techniques, multi-scale geometric analysis methods such as Curvelet transform, Contourle transform and Shearlet transform have been widely valued and studied in image denoising. Among them, the Shearlet tr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 李映陈瑞鸣胡杰张艳宁
Owner 西安谦腾进科技有限公司
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