文件名称：ECCV2018 超分辨相关.7z
文件大小：6.89MB
文件格式：7Z
更新时间：2023-06-01 03:28:32
超分辨
亲历了ECCV 2018的机器学习研究员Tetianka Martyniuk挑选了6篇ECCV 2018接收论文，概述了超分辨率（Super-Resolution, SR）技术的未来发展趋势。内容包括：一：学习图像超分辨率，先学习图像退化；二：由面部五官热力图指导的面部超分辨率；三：用深度残差通道的注意网络的图像超分辨率；四：用于图像超分辨率的多尺度残差网络；五：级联残差加持的快速、准确、轻量级的超分辨率网络；六：SRFeat：具有特征识别的单个图像超分辨率
【文件预览】：
ECCV2018 超分辨相关
----Single Image Super-Resolution with Feature Discrimination.pdf(1.22MB)
----To learn image super-resolution, use a GAN to learn how to do image degradation first.pdf(3.12MB)
----Multi-scale Residual Network for Image Super-Resolution.pdf(1.57MB)
----Image Super-Resolution Using Very Deep Residual Channel Attention Networks.pdf(862KB)
----Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network.pdf(942KB)
----Face Super-resolution Guided by Facial Component Heatmaps.pdf(784KB)