基于γ-Clahe和Real-esrgan的红外图像增强方法

Method of infrared image enhancement based onγ-Clahe and Real-esrgan

针对红外图像对比度低和清晰度差的问题,提出一种基于γ-Clahe和Real-esrgan的红外图像增强方法。通过Haar小波变换将红外图像分解为低频和高频分量,对低频和高频分量分别进行γ-Clahe变换和高斯滤波,将处理后低频和高频分量进行重构得到重构的红外图像,采用Real-esrgan算法对重构的红外图像进行超分辨率重建。结果表明,所提出的红外图像增强算法的主观和客观指标均优于HE、Clahe和Gamma算法,相较于上述三种传统算法PSNR平均提高了3.525、9.141和9.631,SSIM平均提高了0.085、0.295和0.162,使重建后的红外图像对比度和清晰度得到了增强。

This paper proposes a infrared image enhancement method based onγ-Clahe and Real-esrgan to address the low contrast and poor clarity of infrared image.The study involves initially decomposing the infrared image into low-frequency and high-frequency components through Haar wavelet transform;subjecting the low-frequency componenttoγ-Clahe transformation,while filtering the high-frequency component by Gaussian;reconstructing the processed components to generate the enhanced infrared image;and subjecting the enhanced image to super-resolution reconstruction by using Real-esrgan algorithm.The experimental results indicate that the proposed method outperforms traditional techniques such as HE,Clahe,and Gamma algorithms in both subjective and objective assessments.Specifically,compared to the three traditional methods,the proposed approach yields average improvements in PSNR by 3.525,9.141 and 9.631,and in SSIM by 0.085,0.295 and 0.162,respectively,as which significantly enhances the contrast and clarity of the reconstructed infrared images.