基于边窗滤波的高动态红外图像压缩增强算法

High Dynamic Infrared Image Compression and Enhancement Algorithm Based on Side Window Filtering

红外热成像系统采集的数据大都是高动态范围,为了实现高动态红外图像的可视化,动态范围压缩和细节增强技术的研究至关重要。针对传统方法存在的梯度反转伪影、低对比度细节丢失、背景噪声过增强等问题,提出一种基于边窗滤波的高动态红外图像压缩增强方法。首先,采用边窗滤波将原始红外图像分解为基础分量和细节分量;然后,根据基础分量的灰度级分布情况,设计一种自适应阈值的平台直方图算法,对基础分量进行压缩;接着,利用双边滤波器核权重分布特点,生成自适应增益系数,对细节分量进行增强;最后,对基础分量和细节分量进行加权融合,并将结果量化到8位动态范围。实验结果表明,与经典的压缩增强方法相比,所提方法对强边缘具有更好的保边效果,可以有效避免梯度反转伪影和光晕问题,细节信息更丰富,背景噪声抑制效果更好,对不同场景的适应性更强。

Data collected via infrared thermal imaging systems are primarily in high dynamic range.Thus,research on dynamic range compression and detail enhancement technology is crucial to achieve visualization of high dynamic infrared images.This paper addresses the challenges of gradient reversal artifacts,low contrast detail loss,and background noise over enhancement in traditional methods.In this paper,we propose a high dynamic infrared image compression and enhancement method based on side window filtering.First,side window filtering is used to decompose the original infrared image into basic and detail components.Then,an adaptive threshold platform histogram algorithm is designed based on the grayscale distribution of the basic component in order to compress the basic component.The detail component is enhanced using the adaptive gain coefficient generated via the weight distribution characteristics of the bilateral filter core.Finally,the basic and detail components are weighed and fused and quantified to an 8-bit dynamic range.According to experimental results,compared with classic compression enhancement methods,the proposed method has a superior edge preservation effect on strong edges,can effectively avoid gradient inversion artifacts and halo problems,and has richer detail information,better background noise suppression effect,and stronger adaptability to different scenes.