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Image fusion method based on convolutional neural network and dynamic guided filtering

A convolutional neural network and guided filtering technology, applied in the field of image fusion algorithms of multi-focus images, can solve the problems of low efficiency, relying on manual design, and high algorithm complexity, so as to solve the problems of complex algorithms, unsatisfactory fusion effects, and fusion. The effect of quality improvement

Inactive Publication Date: 2019-12-10
NORTHWESTERN POLYTECHNICAL UNIV +1
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Problems solved by technology

[0005] Aiming at the problems of high algorithm complexity, low efficiency and over-reliance on manual design in traditional multi-focus image fusion, a new method of multi-focus image fusion based on convolutional neural network and dynamic guided filtering is proposed.

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  • Image fusion method based on convolutional neural network and dynamic guided filtering
  • Image fusion method based on convolutional neural network and dynamic guided filtering
  • Image fusion method based on convolutional neural network and dynamic guided filtering

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Embodiment Construction

[0024] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0025] The present invention is based on the image fusion method of convolutional neural network and dynamic guided filtering, and the specific implementation process is as follows: focus detection based on convolutional neural network

[0026] CNN is a typical deep learning model, which learns a hierarchical feature representation mechanism for signal or image data with different levels of abstraction. CNN is a trainable multi-stage feed-forward artificial neural network, each stage contains a certain number of feature maps, which correspond to the abstraction level of the feature.

[0027] Local receptive fields, shared weights, and subsampling are the three basic architectural ideas of CNN. The local receptive field indicates that neurons in a certain stage are only connected to several spatially adjacent neurons in the previous stage, and the connections of ...

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Abstract

The invention relates to a new multi-focus image fusion method based on a convolutional neural network and dynamic guided filtering. By constructing a convolutional neural network for focus detection,direct mapping from a source image to a focus image is generated, manual operation is avoided, pixel distribution maps of a focusing area and a non-focusing area are obtained, and then a high-qualityfused image is obtained through dynamic guided filtering operation of small area removal and edge reservation.

Description

technical field [0001] The invention relates to an image fusion algorithm of multi-focus images, which can be applied to various military or civilian image processing systems. Background technique [0002] As an aerial reconnaissance and weapon platform, drones usually carry a variety of imaging sensors, such as infrared, visible light, laser, SAR and other sensors. The purpose is to comprehensively utilize multi-sensor image information to enable drones to better perform aerial reconnaissance, Battlefield surveillance and other tasks. Image fusion technology is to integrate the image information obtained by various imaging sensors, and use the complementary information between imaging sensors to obtain a clear image of the target and the scene, which will be very beneficial to the precise reconnaissance of ground targets by drones, identification and positioning etc. The fused image has higher definition, larger amount of information, more comprehensive target and scene i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50
CPCG06T5/50G06T2207/10148G06T2207/20081G06T2207/20084
Inventor 王健杨珂秦春霞
Owner NORTHWESTERN POLYTECHNICAL UNIV
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