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Image denoising method based on multi-resolution singular value decomposition

A singular value decomposition and multi-resolution technology, applied in the field of image denoising, can solve problems such as loss, loss of partial image features, multi-image texture information, etc., and achieve the effect of easy implementation and simple process

Inactive Publication Date: 2016-06-22
ZHEJIANG UNIVERSITY OF MEDIA AND COMMUNICATIONS
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Problems solved by technology

[0005] In the existing denoising process, more image texture information will be lost, and some features of the image will be lost in the denoising process.

Method used

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  • Image denoising method based on multi-resolution singular value decomposition
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  • Image denoising method based on multi-resolution singular value decomposition

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Embodiment Construction

[0021] The present invention will be described in detail below with reference to the accompanying drawings.

[0022] like figure 1 As shown, an image denoising method based on multi-resolution singular value decomposition includes the following steps:

[0023] (1) Adjust the size of the noise image I whose original size is M×N to become

[0024] In specific implementation, M=N=256, and the adjusted image size is 4×16384, denoted as I 1 .

[0025] (2) For I 1 Do SVD decomposition:

[0026] [US]=SVD(I 1 )

[0027] Among them, SVD represents the singular value decomposition operation; U and S represent the left singular matrix and diagonal matrix with a size of 4 × 4 obtained by the final decomposition, respectively. The diagonal elements of the diagonal matrix are the singular values ​​arranged in descending order, and the size is 4 × 16384.

[0028] (3) Calculate the new matrix Y according to the following formula:

[0029] Y=U T I 1

[0030] Among them, U is the ...

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Abstract

The invention discloses an image denoising method based on multi-resolution singular value decomposition. The method comprises the following steps of step1, adjusting a size of an original image, and carrying out singular value decomposition on an adjusted image data matrix to acquire a left singular matrix; step2, carrying out product on a transposed matrix of the left singular matrix and the adjusted image data matrix to acquire a new image data matrix; step3, carrying out size adjusting on each row of data of the new image data matrix; step4, taking a low frequency portion as an original image and repeatedly carrying out at least once process from the step1 to the step3; step5, using a threshold denoising rule to process a matrix corresponding to a high frequency portion, recombining a processing result into the image data matrix with the same size with the original image and acquiring a denoised image. In the invention, when the noise is removed, simultaneously, high frequency information of the image can be well kept, and a process is simple and is easy to carry out.

Description

technical field [0001] The invention relates to the technical field of image denoising, in particular to an image denoising method based on multi-resolution singular value decomposition. Background technique [0002] In the process of image generation, transmission, storage, etc., it is inevitable to be affected by noise. Once the image has noise, not only its visual effect will be deteriorated, but its own characteristics will also be damaged. [0003] Today's digital cameras generally use CCD or CMOS to generate images. Due to the unsatisfactory working state of electronic components and the influence of ambient light, the generated images are often noisy, and these noises can generally be generated by Gaussian models or pulses. Model noise. [0004] In recent years, there has been a lot of research work in the field of image denoising, and many denoising algorithms have been proposed, but the problem of denoising still exists. For impulse noise, median filtering and its...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/20182G06T5/70
Inventor 张根源
Owner ZHEJIANG UNIVERSITY OF MEDIA AND COMMUNICATIONS
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