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Dictionary learning algorithm based on sparse model analysis

A dictionary learning and sparse model technology, applied in the field of signal processing, can solve problems such as estimating source signals in advance, achieve accurate target and background information, improve quality, and improve computing efficiency.

Inactive Publication Date: 2014-09-03
NANCHANG UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the existing dictionary learning algorithm needs to estimate the source signal in advance, and proposes a dictionary learning algorithm based on an analytic sparse model, which directly adjusts the dictionary according to the noise signal without estimating the source signal in advance Signal

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Embodiment Construction

[0027] Preferred embodiments of the present invention are further explained in detail below.

[0028] The present invention directly uses the noise signal to construct the cost function, and uses the gradient descent method to solve the cost function, so as to ensure that the value solved by each iteration is optimal in a local range. The algorithm is used to update the dictionary adaptively to form a super-complete dictionary that can better represent the image structure. Then use the OBG algorithm to estimate the source signal for image denoising. The specific operation steps are as follows:

[0029] (1) Extract from the noisy image K indivual The size of the image sub-blocks, the sub-blocks are arranged in columns to obtain the training data matrix .

[0030] (2) Randomly generate an initial dictionary .

[0031] (3) Use the training data and the initial dictionary to construct an optimization function for solving sparse coefficients, as follows:

[0032] ...

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Abstract

The invention relates to a dictionary learning algorithm based on sparse model analysis. According to the algorithm, a cost function is established directly through noise signals, the cost function is solved through a gradient descent method, and it is guaranteed that the value obtained through iteration conducted each time is optimal within the local range. A dictionary is adaptively updated through the algorithm, so that a super-complete dictionary capable of better expressing the image structure is formed. The dictionary learning algorithm can be used for image noise removal, image quality can be improved, more accurate target and background information can be provided, and the ideal noise removal effect can be achieved. The algorithm is widely applied to the military field and non-military field of target detection, optical imaging, safety monitoring systems and the like.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and relates to a dictionary learning algorithm based on an analytic sparse model, which is applied to image denoising. Background technique [0002] With the development and popularization of digital image technology, digital images are more widely and deeply used in various fields of people's lives. This requires people to obtain more innovation and development in the field of digital images. There are many factors affecting image quality and visual effects in the process of digital image acquisition and processing. As one of the main reasons, noise has great harm to digital images, such as: affecting the visual effect of digital images, covering up the detailed information in digital images, and interfering with feature extraction and target recognition of digital images. Therefore, image denoising processing, as a very important part of the image processing process, has very importa...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 张烨王浩龙龚黎华张文全
Owner NANCHANG UNIV
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