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Titlebook: Computer Vision and Image Processing; 7th International Co Deep Gupta,Kishor Bhurchandi,Sanjeev Kumar Conference proceedings 2023 The Edito

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21#
發(fā)表于 2025-3-25 04:20:10 | 只看該作者
Advances in Cryptology – CRYPTO 2016ved under the framework of primal-dual algorithms. Experimental evaluation shows that the proposed method can significantly improve the restoration quality of the images, compared to the existing techniques.
22#
發(fā)表于 2025-3-25 08:54:22 | 只看該作者
,Hiding Video in?Images: Harnessing Adversarial Learning on?Deep 3D-Spatio-Temporal Convolutional Nege. (2) An extractor that reverse-engineers the embedder function to extract the hidden data inside the encoded image. A multi-discriminator GAN framework with multi-objective training for multimedia hiding is one of the novel contributions of this work.
23#
發(fā)表于 2025-3-25 15:30:51 | 只看該作者
,A Bayesian Approach to?Gaussian-Impulse Noise Removal Using Hessian Norm Regularization,ved under the framework of primal-dual algorithms. Experimental evaluation shows that the proposed method can significantly improve the restoration quality of the images, compared to the existing techniques.
24#
發(fā)表于 2025-3-25 19:06:03 | 只看該作者
,Left Ventricle Segmentation of?2D Echocardiography Using Deep Learning,his paper. On the CAMUS dataset, the Vgg16 Unet model is new, and it has demonstrated promising results for endocardium segmentation. The dice metric values achieved for endocardium, epicardium, and left atrium are ., ., and . respectively.
25#
發(fā)表于 2025-3-25 22:06:55 | 只看該作者
Conference proceedings 2023, CVIP 2022, held in Nagpur, India, November 4–6, 2022...The 110 full papers and 11 short papers?were carefully reviewed and selected from 307 submissions. Out of 121 papers, 109 papers are included in this book. The topical scope of the two-volume set focuses on Medical?Image? Analysis,? Image/? Vi
26#
發(fā)表于 2025-3-26 03:40:18 | 只看該作者
27#
發(fā)表于 2025-3-26 05:40:37 | 只看該作者
28#
發(fā)表于 2025-3-26 09:21:28 | 只看該作者
29#
發(fā)表于 2025-3-26 15:09:40 | 只看該作者
30#
發(fā)表于 2025-3-26 20:46:49 | 只看該作者
https://doi.org/10.1007/978-3-662-53018-4owards attacks like Gaussian blurring, rotation, noise, and cropping. However, the model can be trained on any possible attacks to reduce noise sensitivity further. In this manuscript, we considered images as both messages and containers. However, the method can be extended to any combination of multi-media data.
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