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An image deblurring method based on multi-parameter regular optimization model

An optimized model, multi-parameter technology, applied in image enhancement, image data processing, instruments, etc., can solve problems such as deblurring of optically defocused images, etc., to achieve rich details and textures, less distortion, and no artifacts.

Active Publication Date: 2019-10-22
TIANJIN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

But the above algorithm is not suitable for deblurring of optically defocused images

Method used

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  • An image deblurring method based on multi-parameter regular optimization model
  • An image deblurring method based on multi-parameter regular optimization model
  • An image deblurring method based on multi-parameter regular optimization model

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Embodiment 1

[0028] An embodiment of the present invention provides an image deblurring method based on a multi-parameter regular optimization model, see figure 1 , the method includes the following steps:

[0029] 101: By combining the Tychonoff regularization term with the Huber function, construct a multi-parameter regularization optimization model, which is used to deblur the image;

[0030] 102: Convert the multi-parameter regular optimization model into the form of augmented Lagrangian function;

[0031] 103: Minimization Algorithms by Alternating Directions [11] Solving the above augmented Lagrange function An all-in-focus image corresponding to the original defocused image is reconstructed.

[0032] In summary, the image deblurring method proposed in the embodiment of the present invention can deblur grayscale images, text images, non-text images and low-light images, and the restored image contains richer detail textures, less distortion, and no Artifact phenomenon, clearer a...

Embodiment 2

[0034] The scheme in embodiment 1 is introduced in detail below in conjunction with specific calculation formulas, see the following description for details:

[0035] 201: Construct a multi-parameter regular optimization model, which is used to deblur the image:

[0036] Among them, the image blurring process is expressed as:

[0037] d=hf+r (1)

[0038] in, is the existing defocused blurred image, h represents the two-dimensional Gaussian blur kernel, is a potential clear image, hf means convolving h and f, and r means additive noise; represents an n 2 dimensional real number space.

[0039] Given a defocused image A multi-parameter regular optimization model is proposed to reconstruct the clear image f, which can be expressed as:

[0040]

[0041] in, is the fidelity item, α is the weight parameter of the balanced fidelity item, μ 1 and μ 2 is the weight parameter of the regular term. is the discrete form of the Tychonoff regularization term, Represents...

Embodiment 3

[0079] The method in embodiment 1 and 2 is verified below in conjunction with specific accompanying drawing, and experimental data, see the following description for details:

[0080] The embodiments of the present invention verify the effectiveness of the algorithm by comparing the subjective experimental results and the evaluation scores of the objective LR criterion. The parameters of the regular optimization model constructed in the embodiment of the present invention are set to: α=22, μ 1 =0.9,μ 2 = 0.1 and γ = 10, Indicates the step size of each update of κ, which is used to ensure the convergence of the regularized optimization model.

[0081] 1. Subjective experiment

[0082] The method proposed in the embodiment of the present invention is respectively compared with the fast image deblurring algorithm [1] , an image deblurring algorithm based on spectral characteristics [3] , an image deblurring algorithm based on total variation [4] , a natural image deblurrin...

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Abstract

The invention discloses an image deblurring method based on a multi-parameter regular optimization model. The image deblurring method comprises the following steps that combining a Tikhonov regular item with a Huber function to construct the multi-parameter regular optimization model, wherein the model is used for carrying out deblurring processing on an image; converting the multi-parameter regular optimization model into an augmented Lagrangian function form; and through an alternating direction minimization algorithm, solving the above augmented Lagrangian function, and reconstructing a total focusing image corresponding to an original defocusing image. By use of the image deblurring method which is put forward by the invention, a grayscale image, a text image, a non-text image and a low-illuminance image can be deblurred, and the recovered image contains richer detail textures and less distortion, is free from an artifact phenomenon and is clearer and more natural.

Description

technical field [0001] The invention relates to the field of image deblurring, in particular to an image deblurring method based on a multi-parameter regular optimization model. The method proposes a multi-parameter regular optimization model, which can be used to reconstruct an all-focus image. Background technique [0002] Image deblurring is an important research topic in the field of computer vision. The image blurring process can be modeled as the convolution of a latent sharp image with an unknown point spread function, which can be used to describe the cause of blurring. Therefore, image deblurring can be defined as a deconvolution process whose main purpose is to solve the above convolution process in reverse and reconstruct a potentially clear image. [0003] For the image deblurring problem, many image deblurring algorithms have emerged in recent years, and these algorithms can be divided into two categories: blind deblurring methods and non-blind deblurring metho...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
CPCG06T5/73Y02T10/40
Inventor 周圆王爱华陈阳冯丽洋侯春萍
Owner TIANJIN UNIV
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