目录 故事背景 建模现实噪声 CBDNet 非对称损失 数据库 实验 发表在2019 CVPR. 摘要 While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy p…

本文提出了一个针对真实图像的盲卷积去噪网络,增强了深度去噪模型的鲁棒性和实用性. 摘要 作者提出了一个 CBD-Net,由噪声估计子网络和去噪子网络两部分组成. 作者设计了一个更加真实的噪声模型,同时考虑了信号依赖的噪声和相机内部处理的噪声. 基于真实噪声模型合成的图片和真实的噪声图片被联合在一起对网络进行训练. 噪声模型 除了高斯噪声,真实的图片噪声更加复杂,并且是信号依赖的. 给定一个干净图片 x,一个更加真实的噪声模型 \(n(x) - N(0, \sigma(y))\) 可以表示为: 其…

转:http://www.sigvc.org/bbs/thread-72-1-1.html 一.特征提取Feature Extraction:   SIFT [1] [Demo program][SIFT Library] [VLFeat]   PCA-SIFT [2] [Project]   Affine-SIFT [3] [Project]   SURF [4] [OpenSURF] [Matlab Wrapper]   Affine Covariant Features [5] [Oxfo…

from:http://www.sigvc.org/bbs/thread-72-1-1.html 一.特征提取Feature Extraction:   SIFT [1] [Demo program][SIFT Library] [VLFeat]   PCA-SIFT [2] [Project]   Affine-SIFT [3] [Project]   SURF [4] [OpenSURF] [Matlab Wrapper]   Affine Covariant Features [5] [O…

This article come from HEREARS-L1: Learning Tuesday 10:30–12:30; Oral Session; Room: Leonard de Vinci 10:30  ARS-L1.1—GROUP STRUCTURED DIRTY DICTIONARY LEARNING FOR CLASSIFICATION Yuanming Suo, Minh Dao, Trac Tran, Johns Hopkins University, USA; Hojj…

Topic Name Reference code Feature Detection, Feature Extraction, and Action Recognition Space-Time Interest Points (STIP) I. Laptev, On Space-Time Interest Points, IJCV, 2005 and I. Laptev and T. Lindeberg, On Space-Time Interest Points, IJCV 2005  …

CVPR 2018大会将于2018年6月18~22日于美国犹他州的盐湖城(Salt Lake City)举办. CVPR2018论文集下载:http://openaccess.thecvf.com/menu.py 目前CVPR2018论文还不能打包下载,但可以看到收录论文标题的清单,感兴趣的可以自行google/baidu下载 详细可以点击链接:https://github.com/amusi/daily-paper-computer-vision/blob/master/2018/cvpr20…

目录 故事背景 网络结构 BN和残差学习 拓展到其他任务 发表在2017 TIP. 摘要 Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating t…

论文地址:FLGCNN:一种新颖的全卷积神经网络,用于基于话语的目标函数的端到端单耳语音增强 论文代码:https://github.com/LXP-Never/FLGCCRN(非官方复现) 引用格式:Zhu Y, Xu X, Ye Z. FLGCNN: A novel fully convolutional neural network for end-to-end monaural speech enhancement with utterance-based objective funct…

<DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks>研读笔记 论文标题:DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks 来源:ICCV 2017 摘要: 尽管手机中的嵌入式照相机的性能在快速地发展,但是它们所受到的物理限制——较小的感光器件,精简的镜头和缺少特定的硬件——制约着手机的相机拍出与DSLR(单反)同…