Infrared Image Denoising Algorithm Based on a Rough Set Approach
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摘要: 针对红外图像噪声复杂多变,在抑制噪声的同时,还需要兼顾细节增强的问题,本文提出了一种基于粗糙集的红外图像多维降噪算法。对采集到的红外图像通过引导滤波进行分层后运用粗糙集理论进一步多维度的分层,分别处理后合并还原得到输出图像。综合对比主观观察与客观评价指标,该算法能够对红外图像降噪有良好效果,对弱小目标细节有良好的增强效果,另外,该算法复杂度较低,具有良好的实时性,在工程实现方面具有良好的应用前景。Abstract: With respect to the complexity and variety of infrared image noise, it is necessary to consider the detailed enhancement of images while suppressing noise. Accordingly, this study developed an infrared image denoising algorithm using the rough set theory. Collected infrared images were first layered by guided filtering. Then, further multi-dimensional stratification was conducted using the rough set theory, and output images were obtained through merging and restoration. Compared with a subjective observation and an objective evaluation index, the algorithm was effective in infrared image denoising and helped to enhance weak and small target details. In addition, the algorithm showed low complexity and good real-time performance. It thus has good application prospects in engineering implementations.
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Key words:
- infrared image /
- multi dimensional noise reduction /
- guided filtering /
- rough set /
- image layering
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表 1 不同算法降噪效果客观评价指标
Table 1. Objective evaluation index of noise reduction effect of different algorithms
Algorithm Peak Signal To Noise Ratio(PSNR) Entropy Guided filtering 54.5211 6.1239 Bilateral filtering 55.3598 6.1274 Least square filtering 57.7474 6.3115 The algorithm in this paper 58.7371 6.4126 表 2 不同算法运行时间对比
Table 2. Running time comparison of different algorithms
Algorithm Time complexity Running time/s Guided filtering O(N) 2.744 Bilateral filtering O(σ2) 5.273 Least square filtering O(mN) 4.397 The algorithm in this paper O(N) 3.174 -
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