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Adaptive image enhancement algorithm based on the model of surface roughness detection system

机译:基于表面粗糙度检测系统模型的自适应图像增强算法

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Abstract In view of the relatively high noise interference and halo phenomenon of the traditional adaptive image enhancement algorithm based on the unsharp masking method, a kind of adaptive image enhancement algorithm based on the integration of the model of surface roughness detection system (hereinafter referred to as MSRDS for short) is put forward in this paper. Through the design of the model of the surface roughness detection system, non-linear segmentation, denoising, and adaptive amplification are carried out on the details of the image under this system model. The dynamic range compressed image base layer and the adaptively enhanced image detail layer are non-linearly superimposed to obtain the final enhanced image. Finally, through the comparative experiment analysis, it demonstrates that the method put forward in this paper can suppress the interference noise and the halo phenomenon of the image very well while carrying out dynamic range compression and detail amplification of the adaptive image effectively. And the result thus obtained is very suitable for the back-end image processing of the actual thermal infrared imager.
机译:摘要鉴于基于unsharp掩蔽方法的传统自适应图像增强算法的相对高的噪声干扰和光环现象,一种基于表面粗糙度检测系统模型集成的自适应图像增强算法(以下简称短暂的MSRDS)提出了本文。通过表面粗糙度检测系统的模型,在该系统模型下的图像的细节上进行非线性分割,去噪和自适应放大。动态范围压缩图像基层和自适应增强的图像细节层是非线性叠加的,以获得最终增强图像。最后,通过对比实验分析,表明本文提出的方法可以抑制图像的干扰噪声和晕圈现象,同时进行动态范围压缩和有效的自适应图像的细节放大。由此获得的结果非常适合于实际热红外成像器的后端图像处理。

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