针对红外图像纹理不清晰、亮度低、高噪声的问题,提出了一种自适应多特征融合的红外图像增强算法.首先,通过用自动线性映射的方法对14位红外图像进行有效特征提取得到了 16位图像,提升了图像可视化效果.其次,引入广义反锐化掩模(Generalized Unsharp Masking,GUM)算法与带色彩恢复的多尺度视网膜(Multi-Scale Retinex with Color Restoration,MSRCR)增强算法联合处理的方法,获得图像不同尺度的有效信息,提升了图像的对比度.最后设计了自适应权重图,并结合图像金字塔结构的特性,对不同特征层进行有效信息的互补融合,提升了图像亮度,丰富了图像的纹理信息.实验结果表明,此算法有效提升了红外图像的对比度和视觉效果;相较于现有的几种算法,其平均梯度(Average Gradient,AG)约提升 0.6％,峰值信噪比(Peak Signal-to-Noise Ra-tio,PSNR)约提升10％,图像的边缘信息有效率约提升11％,图像的清晰度约提升10％.
Infrared Image Enhancement Algorithm Based on Adaptive Multi-Feature Fusion
Aiming at the problems of unclear texture,low brightness and high noise of infrared images,an a-daptive multi-feature fusion algorithm for infrared image enhancement is proposed in this paper.Firstly,the automatic linear mapping method is used to extract 16-bit infrared images from 14-bit infrared images,which improves the visual effect.Secondly,the combined processing method of GUM and MSRCR is introduced to obtain effective information on different scales of the image and improve the contrast of the image.Finally,the adaptive weight map is designed and combined with the characteristics of the image pyramid structure to com-plement and fuse the effective information of different feature layers,which improves the brightness of the im-age and enriches the texture information of the image.Experimental results show that this algorithm can effec-tively improve the contrast and visual effect of infrared images,and its AG is increased by about 0.6％com-pared with the existing algorithms.The PSNR is about 10％higher,the image edge information efficiency is about 11％higher,and the image sharpness is about 10％higher.
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