chronicle 发表于 2025-3-28 15:59:03
Die Aufgaben der Kostenrechnung,etter focus on and utilize feature information at different scales, and achieves effective skip connections. The proposed model is evaluated on two different medical image segmentation datasets, and the results show that our model has achieved better performance in terms of accuracy.指数 发表于 2025-3-28 19:30:43
http://reply.papertrans.cn/17/1672/167162/167162_42.png缩短 发表于 2025-3-29 02:56:30
https://doi.org/10.1007/978-3-322-84098-1to recover the secret image. The experimental outcomes indicate that the proposed model increases the visual effect of images, with cover images PSNR and SSIM reaching 40.36 dB and 98.18%, respectively. Therefore, the model can effectively hide images during information transmission and prevent attaCumbersome 发表于 2025-3-29 05:37:28
http://reply.papertrans.cn/17/1672/167162/167162_44.png小画像 发表于 2025-3-29 08:08:01
Grundlagen der Lebensmittelmikrobiologie, reconstruction is performed using an inverse wavelet transformation. Experimental results demonstrate that the proposed algorithm not only effectively suppresses complex noise in images and enhances the contrast of clinical pulmonary CT images but also preserves the natural appearance of images an不舒服 发表于 2025-3-29 12:23:47
Grundlagen der Lebensmittelmikrobiologien the first stage, we introduce a novel two-decoder architecture with collaborative learning to preliminarily decouple blur features and mitigate the learning complexity of the network. In the second stage, we propose a coupled learning module (CLM) and a feature enhancement block (FEB) to constrain荣幸 发表于 2025-3-29 18:49:35
http://reply.papertrans.cn/17/1672/167162/167162_47.pngCollected 发表于 2025-3-29 22:17:18
http://reply.papertrans.cn/17/1672/167162/167162_48.pngMosaic 发表于 2025-3-30 01:47:51
http://reply.papertrans.cn/17/1672/167162/167162_49.png构成 发表于 2025-3-30 08:03:26
MAPNet: A Multi-scale Attention Pooling Network for Ultrasound Medical Image Segmentationetter focus on and utilize feature information at different scales, and achieves effective skip connections. The proposed model is evaluated on two different medical image segmentation datasets, and the results show that our model has achieved better performance in terms of accuracy.