Nonlinear enhancement of multi-focus infrared image based on depth neural network
When there are multiple focused target subjects in the infrared image,it will lead to a decrease in the quality of the infrared image and blurring in some areas.Therefore,a deep neural network-based nonlinear enhance-ment method for multifocal infrared images is proposed.Under the guidance of guided filtering,detail layer images and background layer images are obtained from infrared images,and a local clarity evaluation function is established in the detail layer image to obtain clear detail layer and background layer,and the two are fused.Using the constructed deep neural network structure,establish a nonlinear gain function,and achieve nonlinear enhancement of multi focus infra-red images by setting thresholds and adjusting subband coefficients.The test results show that the proposed method did not change the brightness and texture fluctuations of the original image during the enhancement process;The informa-tion entropy of the enhanced image is about 57％higher than that of the original image.The image anti noise perform-ance value is higher,with an average of 7.4 dB.The image is clearer,and the SSIM value is closer to 1,with an av-erage of 0.98.The enhanced image has a higher similarity with the original image,which is closer to the real image.
multi focus infrared imagesdetail layer imageslocal clarity evaluation functionsubband coeffi-cientnonlinear gain function
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