An improved pulse coupled neural networks model for semantic IoT

Ma, Rong; Zhang, Zhen; Ma, Yide; Hu, Xiping; Ngai, Edith C.-H.; Leung, Victor C. M.

doi:10.1016/j.dcan.2023.06.010

Cited by 3 publications

(1 citation statement)

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“…Therefore, obtaining both low-level and high-level features of an image simultaneously is beneficial for generating more appropriate semantic information. [23,24]. In addition, since the inpainted image needs to be as consistent as possible with human visual perception, the traces of inpainting that are imperceptible to humans are inpainted.…”

Section: Introductionmentioning

confidence: 99%

Progressively Multi-Scale Feature Fusion for Image Inpainting

Wen,

Li,

Tolba

et al. 2023

Mathematics

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The rapid advancement of Wise Information Technology of med (WITMED) has made the integration of traditional Chinese medicine tongue diagnosis and computer technology an increasingly significant area of research. The doctor obtains patient’s tongue images to make a further diagnosis. However, the tongue image may be broken during the process of collecting the tongue image. Due to the extremely complex texture of the tongue and significant individual differences, existing methods fail to fully obtain sufficient feature information, which result in inaccurate inpainted tongue images. To address this problem, we propose a recurrent tongue image inpainting algorithm based on multi-scale feature fusion called Multi-Scale Fusion Module and Recurrent Attention Mechanism Network (MSFM-RAM-Net). We first propose Multi-Scale Fusion Module (MSFM), which preserves the feature information of tongue images at different scales and enhances the consistency between structures. To simultaneously accelerate the inpainting process and enhance the quality of the inpainted results, Recurrent Attention Mechanism (RAM) is proposed. RAM focuses the network’s attention on important areas and uses known information to gradually inpaint image, which can avoid redundant feature information and the problem of texture confusion caused by large missing areas. Finally, we establish a tongue image dataset and use this dataset to qualitatively and quantitatively evaluate the MSFM-RAM-Net. The results shows that the MSFM-RAM-Net has a better effect on tongue image inpainting, with PSNR and SSIM increasing by 2.1% and 3.3%, respectively.

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Section: Introductionmentioning

confidence: 99%