An unsupervised image fusion method based on deep learning

Abstract

An unsupervised image fusion method based on deep learning comprises the following steps that visible light and infrared images are obtained, the images are preprocessed through a computer, a data setused for training an image fusion network is constructed, and the data set comprises paired infrared and visible light images; A lightweight deep convolutional neural network is constructed, and thenetwork can perform weighted fusion and decoding reduction on input visible light and infrared images; A hybrid loss function is constructed, the hybrid loss function comprises image generation loss and structure loss, the hybrid loss function is used for training a deep convolutional neural network, and depth image fusion network model parameters are obtained; After model learning is finished, the decoding network is removed, visible light and infrared images can be input through the network, and the output of the network is the fused image. According to the invention, a lightweight image fusion method is realized, and a high-quality fusion effect can be achieved in mobile equipment and embedded equipment 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.

Images