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

• Published in: Microprocessors and microsystems 2021-04, Vol.82, p.103862, Article 103862
• Main Authors: Lv, Hui, Li, Haijun
• Format: Article
• Language: English
• Subjects: Diffusion; Illumination; Image denoising; Image segmentation; Improved canny; Low illumination underwater motion; Noise control; Noise reduction; Similarity; Underwater; Visual effects
• ISSN: 0141-9331; 1872-9436
• DOI: 10.1016/j.micpro.2021.103862

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.