者变
发表于 2025-3-25 07:22:55
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induct
发表于 2025-3-25 11:02:01
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N斯巴达人
发表于 2025-3-25 12:21:24
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insincerity
发表于 2025-3-25 17:15:48
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裹住
发表于 2025-3-25 21:02:03
https://doi.org/10.1007/978-3-662-65102-5 network. Experimental results on large-scale dataset demonstrate the effectiveness of the proposed model against the state-of-the-art (SOTA) SR methods. Notably, when parameters are less than 320k, A.F outperforms SOTA methods for all scales, which proves its ability to better utilize the auxiliary features. Codes are available at ..
防御
发表于 2025-3-26 03:21:39
Image Inpainting with Onion Convolutions an efficient implementation. As qualitative and quantitative comparisons show, our method with onion convolutions outperforms state-of-the-art methods by producing more realistic, visually plausible and semantically coherent results.
Obsessed
发表于 2025-3-26 06:37:23
CS-MCNet: A Video Compressive Sensing Reconstruction Network with Interpretable Motion Compensationo . better than state-of-the-art methods. In addition, due to the feed-forward architecture, the reconstruction can be processed by our network in real time and up to three orders of magnitude faster than traditional iterative methods.
设施
发表于 2025-3-26 09:16:35
Restoring Spatially-Heterogeneous Distortions Using Mixture of Experts Networkresentations. Our model is effective for restoring real-world distortions and we experimentally verify that our method outperforms other models designed to manage both single distortion and multiple distortions.
脆弱么
发表于 2025-3-26 15:03:47
Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Networklf-supervision and a perceptual loss for content preservation. In addition to qualitative evaluation, we design an image quality assessment network to rank the dehazed images. Experimental results on both real and synthetic test data demonstrate that the proposed method performs superiorly against several state-of-the-art land dehazing methods.
maintenance
发表于 2025-3-26 17:08:04
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