Infrared and visible light image fusion method based on DLatLRR and VGG.Net

An image fusion and visible light technology, applied in the image field, can solve the problem that imaging is easily affected by the environment, and achieve the effect of rich detail information, low noise influence, and high definition

Inactive Publication Date: 2020-11-24
LANZHOU JIAOTONG UNIV
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Problems solved by technology

The image formed by the visible light sensor has high spatial resolution and rich background information, but the imaging is easily affected by the environment; the image formed by the infrared sensor is of poor quality, but the image is stable, and the correlation between the two in space and time is complementary to the information in the scene description Sex provides the possibility for its fusion

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  • Infrared and visible light image fusion method based on DLatLRR and VGG.Net
  • Infrared and visible light image fusion method based on DLatLRR and VGG.Net
  • Infrared and visible light image fusion method based on DLatLRR and VGG.Net

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Embodiment 1

[0027] The invention proposes an image fusion algorithm combining low-rank representation decomposition and deep neural network. First, a latent low-rank representation DLatLRR is performed on the source image to obtain the corresponding low-rank part, salient part and sparse noise. Then, the VGG Net model and the joint feature weighting algorithm are used to fuse the low-rank part and the salient part respectively, and the sparse noise of the two is discarded. Finally, image reconstruction is performed on the fused low-rank part and salient part to obtain the final fused image.

[0028] Suppose the infrared image to be fused is IR, the visible light image is VIS, and the fused image is F. The fusion of images using the algorithm of the present invention can be roughly divided into steps such as image decomposition, fusion of decomposed parts, and image reconstruction. The specific content is as follows:

[0029] Step 1: Image Decomposition

[0030] The mathematical model ...

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Abstract

The invention relates to the field of images, in particular to an infrared and visible light image fusion method based on DLatLRR and VGG.Net. The problems of serious feature loss and non-prominent salient target in infrared and visible light image fusion are effectively solved. A source image is decomposed into a low-rank part, a salient part and sparse noise through DLatLRR, the low-rank part isfused based on a VGG16 feature extraction algorithm, and finally image reconstruction is performed on the fused low-rank part and salient part so as to obtain a fused image. The fused image of the algorithm is high in definition, distinct in hierarchy, rich in detail information and less affected by noise, the contrast of the image is high, and the salient region and the target in the scene are relatively more prominent.

Description

technical field [0001] The invention relates to the image field, in particular to an infrared and visible light image fusion method based on DLatLRR and VGG·Net. Background technique [0002] As an important branch of information fusion technology, image fusion can integrate effective information contained in multiple images to obtain fused images with higher spatial and spectral resolutions. The image formed by the visible light sensor has high spatial resolution and rich background information, but the imaging is easily affected by the environment; the image formed by the infrared sensor is of poor quality, but the image is stable, and the correlation between the two in space and time is complementary to the information in the scene description Sex provides the possibility for its integration. [0003] At present, many scholars at home and abroad have studied the fusion algorithm of infrared and visible light images. For example, filter-based image fusion uses filter alg...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50G06N3/04G06K9/42G06T5/00
CPCG06T5/50G06T5/002G06T2207/20221G06T2207/10048G06T2207/20084G06V10/32G06N3/045
Inventor 沈瑜陈小朋王新新刘景春张泓国王霖王海龙梁丽李丹丹
Owner LANZHOU JIAOTONG UNIV
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