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首页> 外文期刊>Applied Sciences >A Non-Reference Image Denoising Method for Infrared Thermal Image Based on Enhanced Dual-Tree Complex Wavelet Optimized by Fruit Fly Algorithm and Bilateral Filter
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A Non-Reference Image Denoising Method for Infrared Thermal Image Based on Enhanced Dual-Tree Complex Wavelet Optimized by Fruit Fly Algorithm and Bilateral Filter

机译：基于果蝇算法和双边滤波器优化的增强双树复小波的红外热像非参考图像降噪方法

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摘要

To eliminate the noise of infrared thermal image without reference and noise model, an improved dual-tree complex wavelet transform (DTCWT), optimized by an improved fruit-fly optimization algorithm (IFOA) and bilateral filter (BF), is proposed in this paper. Firstly, the noisy image is transformed by DTCWT, and the noise variance threshold is optimized by the IFOA, which is enhanced through a fly step range with inertia weight. Then, the denoised image will be re-processed using bilateral filter to improve the denoising performance and enhance the edge information. In the experiment, the proposed method is applied to eliminate both addictive noise and multiplicative noise, and the denoising results are compared with other representative methods, such as DTCWT, block-matching and 3D filtering (BM3D), median filter, wiener filter, wavelet decomposition filter (WDF) and bilateral filter. Moreover, the proposed method is applied as pre-processing utilization for infrared thermal images in a coal mining working face.
机译：为了消除没有参考模型和噪声模型的红外热像噪声，提出了一种改进的双树复小波变换（DTCWT），并通过改进的果蝇优化算法（IFOA）和双边滤波器（BF）进行了优化。 。首先，通过DTCWT对噪声图像进行变换，并通过IFOA对噪声方差阈值进行优化，并通过具有惯性权重的飞跃步距范围进行增强。然后，将使用双边滤波器对降噪后的图像进行重新处理，以提高降噪性能并增强边缘信息。在实验中，将所提出的方法用于消除成瘾噪声和乘法噪声，并将去噪结果与其他代表性方法进行了比较，例如DTCWT，块匹配和3D滤波（BM3D），中值滤波器，维纳滤波器，小波分解过滤器（WDF）和双边过滤器。此外，该方法被用作煤矿工作面红外热图像的预处理方法。

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