An Unsupervised Image Fusion Method Based on Deep Learning

Abstract

An unsupervised image fusion method based on deep learning, comprising the following steps: obtaining visible light and infrared images, and using a computer to preprocess the images, constructing a data set for training an image fusion network, the data set contains pairs of infrared and visible light images; construct a lightweight deep convolutional neural network, the network can achieve weighted fusion and decoding restoration of the input visible light and infrared images; construct a mixed loss function, the mixed loss function includes image generation loss and structure loss , use the mixed loss function to train the deep convolutional neural network to obtain the parameters of the deep image fusion network model; after the model learning is completed, remove the decoding network, you can use the network to input visible light and infrared images, and the output of the network is the fused image . The invention realizes a lightweight image fusion method, and can achieve high-quality fusion effects in mobile devices and embedded devices with limited computing resources.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an unsupervised image fusion method based on deep learning. Background technique [0002] With the development of information technology, digital images are widely used in various scenarios. However, the use of multiple sensors also brings redundancy of information and increased complexity of analysis. How to make better comprehensive use of multi-source sensing information, merge multi-source redundant information, and construct more fused information at the same time has become a key problem that scientists need to solve urgently. Image fusion is one of the key issues in complex detection systems. Its purpose is to use specific algorithms to synthesize multiple source images of the same scene into a new image with more complete information. Although image fusion has been studied for a long time, due to limitations in practical applications, current fusion...

AI Technical Summary

Problems solved by technology

However, due to the complex structure of the deep convolutional neural network model and the single fusion strategy, the model requires a large amount of storage and computing resources in practical applications, and it is difficult to apply it to mobile terminals such as mobile phones.

At the same time, since there is no strict evaluation index for image fusion results, it is difficult to learn from supervised information.

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