Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

者變

induct

N斯巴達(dá)人

insincerity

裹住

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

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

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.

設(shè)施

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

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.

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