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Titlebook: Computer Vision – ECCV 2020 Workshops; Glasgow, UK, August Adrien Bartoli,Andrea Fusiello Conference proceedings 2020 Springer Nature Swit

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51#
發(fā)表于 2025-3-30 10:14:19 | 只看該作者
AWNet: Attentive Wavelet Network for Image ISPpractices among the majority of smartphone users. However, due to the limited size of camera sensors on phone, the photographed image is still visually distinct to the one taken by the digital single-lens reflex (DSLR) camera. To narrow this performance gap, one is to redesign the camera image signa
52#
發(fā)表于 2025-3-30 16:24:21 | 只看該作者
PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processingg, denoising, etc. Deep neural networks have shown promising results over hand-crafted ISP algorithms on solving these tasks separately, or even replacing the whole reconstruction process with one model. Here, we propose PyNET-CA, an end-to-end mobile ISP deep learning algorithm for RAW to RGB recon
53#
發(fā)表于 2025-3-30 18:50:32 | 只看該作者
54#
發(fā)表于 2025-3-30 23:39:02 | 只看該作者
BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokehnd of effect naturally. However, due to the limitation of sensors, smartphones cannot capture images with depth-of-field effects directly. In this paper, we propose a novel generator called Glass-Net, which generates bokeh images not relying on complex hardware. Meanwhile, the GAN-based method and p
55#
發(fā)表于 2025-3-31 02:09:36 | 只看該作者
56#
發(fā)表于 2025-3-31 05:47:28 | 只看該作者
57#
發(fā)表于 2025-3-31 10:54:41 | 只看該作者
CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transferor continuously is a desirable property for virtual try-on applications. We propose a new formulation for the makeup style transfer task, with the objective to learn a color controllable makeup style synthesis. We introduce CA-GAN, a generative model that learns to modify the color of specific objec
58#
發(fā)表于 2025-3-31 15:56:52 | 只看該作者
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