Infrared Image Enhancement Algorithm Based on Unsupervised Learning of Multiple Priors
With the wide application of infrared imaging technology,people's requirements for infrared imaging quality are also increasing.Due to the limitation of infrared imaging principle,infrared images generally have the problems of low contrast and lack of detail texture.Meanwhile,traditional infrared image enhancement algorithms introduce high noise while improving image contrast.Therefore,this paper proposes an unsupervised infrared image enhancement algorithm based on multiple prior knowledge such as Gaussian-Laplacian pyramid,CLAHE and original image,and compares it with other classical prior methods through experiments.Experiments show that the effect of the proposed algorithm is significantly improved compared with the traditional algorithms.It can fully adaptively realize infrared image enhancement operations such as contrast stretching and noise removal,and bring significant improvement to downstream tasks such as target detection.
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