基于引导滤波分层的宽动态范围红外图像细节增强算法

针对宽动态范围红外图像在视觉效果方面出现的对比度低、细节信息不凸显及整体清晰度较差问题,本文提出了一种基于引导滤波分层的宽动态范围红外图像细节增强算法。该算法采用方差决策加权引导滤波对原图作分层,得到了更接近原图的基础层和更精细的细节层。为提高基础层的对比度,首先改进CLAHE的全局剪切点提升增强效果,然后基于AC视觉显著模型指导全局和改进局部直方图的融合,合理兼顾了图像背景和目标；为有效加强细节信息,基于多尺度加权引导滤波得到了信息更全面的新细节层,接着采用梯度域导向滤波对其消噪,再由Sigmoid函数压缩强边缘并突显细微目标,最后将两层信息融合并输出。实验结果表明,该算法在主观视觉和定量指标上均强于对比算法,且自适应强,鲁棒性好。

A wide dynamic range infrared image enhancement algorithm based on guided filter layering

Aiming at the problems of low contrast,unhighlighted detail information and poor overall sharpness of wide dynamic range infrared images in terms of visual effects,a wide dynamic range infrared image detail enhancement algorithm based on the guided filter layering is proposed in this paper.The original image is layered using variance decision weighted guided filtering to obtain a base layer closer to the original image and a finer detail layer.In order to upgrade the contrast of the base layer,the global clipping point of CLAHE are firstly improved to enhance the enhancement effect,and then the integration of global and local histograms is guided based on the AC visual salience model,giving reasonable consideration to the image background and target.Moreover,to enhance the detail information effectively,a new detail layer with more comprehensive information is obtained based on multi scale weighted guided filtering,followed by noise cancellation using gradient domain guided filtering,and then the Sigmoid function is used to compress the strong edge and highlight the subtle target,and finally the information of the two layers is fused and output.The experimental results show that the proposed algorithm is stronger than the comparison algorithm in both subjective vision and quantitative indexes,and has strong adaptability and robustness.