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Image analysis method based on self-adaptive regularization

An image analysis and self-adaptive technology, applied in image enhancement, image data processing, instruments, etc., can solve problems such as unrealizable, high robustness and high efficiency

Inactive Publication Date: 2017-12-12
SHENZHEN WEITESHI TECH
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Problems solved by technology

However, the existing regularization methods cannot achieve high robustness and efficiency for image segmentation, dynamic prediction, image denoising and other issues. The application of the present invention has improved these characteristics very well

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  • Image analysis method based on self-adaptive regularization
  • Image analysis method based on self-adaptive regularization

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Embodiment Construction

[0103] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0104] figure 1 It is a system frame diagram of an image analysis method based on adaptive regularization in the present invention. It mainly includes adaptive regularization methods, applications of adaptive regularization to imaging problems, and overall energy optimization.

[0105] Wherein, the adaptive regularization method promotes the formation of the adaptive regularization method by combining standard regularization methods such as Bayesian or Tikhonov, uses f to represent data such as images or videos, and uses u to represent the Partition the eigenequation or optical flow region of interest, and assume that there is a likelihood function A model that exists in the form of...

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Abstract

The invention provides an image analysis method based on self-adaptive regularization. The image analysis method mainly comprises a self-adaptive regularization method, the application of the self-adaptive regularization to imaging problems and overall energy optimization, and comprises the following processes: the self-adaptive regularization method is used for optimizing compound energy functions generated in image analysis problems, the data fidelity and the regularization degree are automatically balanced according to current data fitting in the process of iterative optimization, a Huber loss function is also required to be used in the data fidelity and regularization items, an efficient convex optimization algorithm based on an alternate direction multiplier algorithm is provided, and by the use of an equivalence relation between the Huber function and a single norm approximation operator, the self-adaptive regularization method is verified in a synthetic or real image through image segmentation, dynamic prediction, image de-noising and other problems; results prove that compared with a conventional image analysis model, a self-adaptive Huber-Huber model applying the method has relatively better robustness and higher efficiency.

Description

technical field [0001] The invention relates to the field of image analysis in computer graphics, in particular to an image analysis method based on adaptive regularization. Background technique [0002] Image analysis based on adaptive regularization is an important research field in digital image processing, and it plays an important role in image processing such as image restoration, image segmentation and image denoising. However, image restoration is an inverse problem from a mathematical point of view, and has a great morbidity, so regularization processing is necessary, and from a statistical point of view, regularization processing is actually a prior information constraint of the image. In addition, the combination of filtering method and regularization technology can well reduce the random noise in the image, the background and the systematic noise of the fringe intensity variation, so that the image output is well optimized. Relevant research results have also be...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/70
Inventor 夏春秋
Owner SHENZHEN WEITESHI TECH
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