Paper | Toward Convolutional Blind Denoising of Real Photographs

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Paper | Toward Convolutional Blind Denoising of Real Photographs

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…

Toward Convolutional Blind Denoising of Real Photographs

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…

Paper Read: Convolutional Image Captioning

Convolutional Image Captioning 2018-11-04 20:42:07 Paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Aneja_Convolutional_Image_Captioning_CVPR_2018_paper.pdf Code: https://github.com/aditya12agd5/convcap Related Papers: 1. Convolutional Se…

Paper Reading - Convolutional Sequence to Sequence Learning ( CoRR 2017 ) ★

Link of the Paper: https://arxiv.org/abs/1705.03122 Motivation: Compared to recurrent layers, convolutions create representations for fixed size contexts, however, the effective context size of the network can easily be made larger by stacking severa…

Paper Reading - Convolutional Image Captioning ( CVPR 2018 )

Link of the Paper: https://arxiv.org/abs/1711.09151 Motivation: LSTM units are complex and inherently sequential across time. Convolutional networks have shown advantages on machine translation and conditional image generation. Innovation: The author…

Paper | U-Net: Convolutional Networks for Biomedical Image Segmentation

目录故事背景 U-Net 具体结构损失数据扩充发表在2015 MICCAI.原本是一篇医学图像分割的论文,但由于U-Net杰出的网络设计,得到了8k+的引用. 摘要 There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and trainin…

读paper:Deep Convolutional Neural Network using Triplets of Faces, Deep Ensemble, andScore-level Fusion for Face Recognition

今天给大家带来一篇来自CVPR 2017关于人脸识别的文章. 文章题目:Deep Convolutional Neural Network using Triplets of Faces, Deep Ensemble, and 摘要: 文章动机:人脸识别在一个没有约束的环境下,在计算机视觉中是一个非常有挑战性的问题.同一个身份的人脸当呈现不同的装饰,不同的姿势和不同的表情都可以使人脸看起来完全不同.这种相同身份的变化可以压倒不同身份的变化,这样给人脸识别带来更大的挑战,特别是在没有约束的环境下.…