A Survey of Uncooled Infrared Image Denoising Algorithms
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摘要: 红外图像处理中,由于非制冷红外探测器工艺技术上的原因,原始的红外图像包含多种噪声,尤其是椒盐噪声、固定或随机条纹噪声。当前有许多红外图像降噪的滤波算法,但在时间、空间、降噪效果、细节保持等方面各有侧重,难以实现完美结合。如何更快速、更高效、更准确地滤除噪声信息,保留更多的细节信息,是今后红外图像处理降噪研究的关键方向。本文调研了目前主流的红外图像降噪算法,并从传统滤波降噪、变换域滤波降噪、基于图像分层处理滤波降噪三大类别进行了分析比较,并且提出了一种结合传统算法和基于图像分层的自适应降噪算法,为今后的相关领域研究人员提供参考。
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关键词:
- 红外图像降噪 /
- 自适应降噪 /
- 传统滤波器 /
- 图像分层滤波器变换域滤波器 /
Abstract: In infrared image processing, owing to technical issues with the infrared detector, the original infrared image includes a variety of noise, especially salt and pepper noise, fixed noise, or random stripe noise. Currently, there are many filtering algorithms for infrared image denoising, but they emphasize time, space, denoising effect, maintaining detail, and so on differently; therefore, it is difficult to achieve a perfect combination. Identifying methods to filter noise information more quickly, efficiently, and accurately and retain more details is an important future research direction for noise reduction in infrared image processing. This study investigated and compared the current mainstream infrared image denoising algorithms from three categories: traditional filter denoising, transform domain filter denoising, and image layered processing filter denoising, and a combination of a traditional algorithm and image layered adaptive denoising algorithm is proposed to provide a reference for future studies in related fields. -
表 1 三类基于分层处理算法优缺点对比
Table 1. Comparison of advantages and disadvantages of three kinds of layered processing algorithms
Type Algorithm Advantages Disadvantages Conventional filtering Gaussian filtering Simple algorithm Insufficient noise reduction effect Mean value filtering Easy to implement, suitable for particle noise Will blur the image Median filtering Good effect on pepper noise treatment Lack of detail noise handling Transformation domain filtering Wavelet transform Good detail retention Algorithm transformation with many steps Contourlet transform Good effect of complex noise treatment Long computation time for algorithms BM3D algorithm Good noise suppression and detail retention Insufficient real-time algorithm Based on image layering filtering Guided filtering Good layering effect and fast running speed Detailing still needs to be improved Bilateral filtering Better background separation Longer running time of the algorithm Weighted least squares Better detail retention Longer processing time -
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