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Denoising method of low illumination underwater motion image based on improved canny

机译:基于改进罐的低照明水下运动图像的去噪方法

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摘要

In order to improve the efficiency of image denoising, this paper proposes a method of image denoising based on improved canny. The region and edge feature fusion method is used to segment the target roughly, and then the fusion threshold of image channel is segmented. According to the result of image segmentation, local intuitionistic fuzzy entropy is extracted on the basis of intuitionistic fuzzy, which is introduced into anisotropic diffusion model and diffusion function. The partial differential noise reduction model is improved and analyzed to obtain the classification information of pixels and realize the noise reduction of low illumination underwater moving image. The experimental results show that the method has a high SNR, and the highest value of structural similarity is 0.92, which proves that the structure similarity between the image after noise reduction and the original image is high, and the visual effect of noise reduction is good, which fully verifies the practical application of the method in image noise reduction.
机译:为了提高图像去噪的效率,本文提出了一种基于改进罐的图像去噪方法。该区域和边缘特征融合方法用于大致分割目标,然后分段图像信道的融合阈值。根据图像分割的结果,基于直觉模糊提取局部直觉模糊熵,其被引入各向异性扩散模型和扩散函数。改进和分析部分差分降噪模型以获得像素的分类信息,并实现低照明水下运动图像的降噪。实验结果表明,该方法具有高SNR,结构相似性的最高值为0.92,证明了降噪后图像与原始图像高的结构相似性,降噪对视觉效果很好,这完全验证了该方法在图像降噪中的实际应用。

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