dual-light image fusion model based on a depth convolution antagonism generation network DCGAN

Abstract

The invention discloses a dual-light image fusion model based on a depth convolution antagonism generation network DCGAN. The model extracts image features of the same target under visible light and infrared light through a depth discrimination convolution network, and sparsely encodes the two image features according to the same feature dictionary; the dual-light image fusion model extracts imagefeatures of the same target under visible light and infrared light through a depth discrimination convolution network. Then the coding feature is fused and used as the input data of the depth convolution generation network, so that the generation network generates the fusion image. Finally, the error training model between the fusion feature and the coding fusion feature is used to generate the dual-light fusion image. The model uses depth learning network to extract and encode the features of visible and infrared images, and the feature points of the two images can be automatically matched by fusing the features of the encoding. The model of the invention can be called at any time after training, and the double-light image with high fusion quality can be automatically generated by inputting visible light image and infrared light image at the same time.

Description

technical field [0001] The invention relates to the technical field of computer image acquisition and synthesis, in particular to a dual-light image fusion model based on a deep convolutional confrontation generation network (DCGAN). Background technique [0002] In recent years, with the continuous development of modern science and technology and the widespread popularization of the Internet, the various functions of temperature night vision monitoring equipment have been greatly improved, and its application cost has also been greatly reduced. Therefore, it has become very popular to use infrared surveillance cameras to obtain temperature information of various industrial machinery and equipment, and many methods of processing infrared surveillance images have also emerged. General infrared detectors mainly perform imaging by receiving the infrared radiation radiated or reflected by the target in the scene, which has a strong penetrating ability to smoke, and still has goo...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a dual-light image fusion model based on deep convolution confrontation generation network DCGAN to solve the problem of poor image fusion effect in traditional image fusion and enhancement methods

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