检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

4,875 篇 会议
92 篇 期刊文献
21 册 图书

馆藏范围

4,988 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,832 篇 工学
- 1,759 篇 计算机科学与技术...
- 1,192 篇 信息与通信工程
- 931 篇 软件工程
- 878 篇 电气工程
- 272 篇 光学工程
- 138 篇 控制科学与工程
- 129 篇 电子科学与技术（可...
- 124 篇 生物工程
- 103 篇 生物医学工程（可授...
- 99 篇 仪器科学与技术
- 85 篇 机械工程
- 68 篇 网络空间安全
- 45 篇 化学工程与技术
- 30 篇 安全科学与工程
- 27 篇 动力工程及工程热...
- 26 篇 测绘科学与技术
1,207 篇 医学
- 1,186 篇 临床医学
- 34 篇 基础医学(可授医学...
- 31 篇 药学(可授医学、理...
- 28 篇 特种医学
1,054 篇 理学
- 755 篇 物理学
- 297 篇 数学
- 138 篇 生物学
- 82 篇 统计学（可授理学、...
- 53 篇 系统科学
- 50 篇 化学
251 篇 管理学
- 160 篇 管理科学与工程(可...
- 98 篇 图书情报与档案管...
47 篇 军事学
- 45 篇 军队指挥学
38 篇 法学
- 34 篇 社会学
13 篇 农学
10 篇 文学
8 篇 经济学
6 篇 教育学
2 篇 艺术学

主题

562 篇 image coding
549 篇 image processing
354 篇 visual communica...
325 篇 visualization
307 篇 feature extracti...
298 篇 image segmentati...
189 篇 image reconstruc...
181 篇 cameras
178 篇 image compressio...
175 篇 humans
161 篇 video coding
161 篇 signal processin...
131 篇 image enhancemen...
131 篇 image quality
128 篇 image color anal...
127 篇 image analysis
123 篇 image edge detec...
122 篇 training
119 篇 image retrieval
107 篇 decoding

机构

36 篇 shanghai jiao to...
29 篇 institute of ima...
25 篇 school of electr...
22 篇 school of electr...
20 篇 university of sc...
18 篇 shanghai jiao to...
16 篇 shanghai jiao to...
16 篇 tianjin univ sch...
16 篇 beijing universi...
13 篇 institute of ima...
12 篇 cas key laborato...
11 篇 university of el...
11 篇 tsinghua univ de...
10 篇 univ sci & techn...
10 篇 school of comput...
10 篇 peking univ inst...
10 篇 tsinghua univ de...
10 篇 xidian univ sch ...
9 篇 zhejiang univers...
9 篇 microsoft res as...

作者

34 篇 zhai guangtao
26 篇 sumei li
25 篇 song li
22 篇 guangtao zhai
22 篇 li sumei
19 篇 li song
18 篇 li li
18 篇 min xiongkuo
16 篇 andré kaup
16 篇 dong liu
16 篇 shan liu
15 篇 yang xiaokang
14 篇 chen zhibo
14 篇 gao wen
13 篇 xie rong
13 篇 xiongkuo min
12 篇 kaup andre
11 篇 heming sun
11 篇 zhibo chen
11 篇 zhenzhong chen

语言

4,900 篇 英文
64 篇 土耳其文
28 篇 中文
10 篇 其他

检索条件"任意字段=Conference on Visual Communications and Image Processing 2007"

共 4988 条记录，以下是301-310 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Adaptive Threshold-based Sparse Representation Network for image Compressive Sensing Reconstruction

Adaptive Threshold-based Sparse Representation Network for I...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Xuan, Yunyi Yang, Chunling Yang, Xin South China Univ Technol Sch Elect & Informat Engn Guangzhou Peoples R China

ISBN: (纸本)9781728185514

Recently, network-based image Compressive Sensing (ICS) algorithms show superior performance in reconstruction quality and speed, yet non-interpretable. Herein, we propose an Adaptive Threshold-based Sparse Representation Reconstruction Network (ATSR-Net), composed of the Convolutional Sparse Representation subnet (CSR-subnet) and the truly Adaptive Threshold Generation subnet (ATG-subnet). The traditional iterations are unfolded into several CSR-subnets, which can fully exploit the local and nonlocal similarities. The ATG-subnet automatically determines a threshold map based on the image intrinsic characterization for flexible feature selection. Moreover, we present a three-level consistency loss based on pixel-level, measurement-level, and feature-level, to accelerate the network convergence. Extensive experiment results demonstrate the superiority of the proposed network to the existing state-of-the-art methods by large margins, both quantitatively and qualitatively.

关键词： image compressive sensing sparse representation adaptive threshold generation

来源：评论

学校读者我要写书评

暂无评论

Video Compression based on Jointly Learned Down-Sampling and Super-Resolution Networks

Video Compression based on Jointly Learned Down-Sampling and...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Wei, Yuzhuo Chen, Li Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781728185514

With the blooming of deep learning technology in computer vision, the integration of deep learning and the traditional video coding has made significant improvements, especially applying the super-resolution neural network as the post-processing module in the down-sampling-based video compression framework. However, the pre-processing module lacks back-propagated gradients for jointly considering down-sampling and up-sampling due to the non-differentiability of the traditional video codec. In this paper, we propose an end-to-end down-sampling-based video compression framework applying convolutional neural networks both as down-sampling and up-sampling. We use a virtual codec neural network to approximate the actual video codec so that the gradient can be effectively back-propagated for joint training. Experimental results show the superiority of our proposed framework compared with the predefined down-sampling-based video compression and various methods of joint training.

关键词： super-resolution video compression deep learning

来源：评论

学校读者我要写书评

暂无评论

Nearly Reversible image-to-image Translation Using Joint Inter-Frame Coding and Embedding

Nearly Reversible Image-to-Image Translation Using Joint Int...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Cao, Xinzhu Yao, Yuanzhi Yu, Nenghai Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China

ISBN: (纸本)9781728185514

image-to-image translation tasks which have been widely investigated with generative adversarial networks (GAN) aim to map an image from the source domain to the target domain. The translated image can be inversely mapped to the reconstructed source image. However, existing GAN-based schemes lack the ability to accomplish reversible translation. To remedy this drawback, a nearly reversible image-to-image translation scheme where the reconstructed source image is approximately distortion-free compared with the corresponding source image is proposed in this paper. The proposed scheme jointly considers inter-frame coding and embedding. Firstly, we organize the GAN-generated reconstructed source image and the source image into a pseudo video. Furthermore, the bitstream obtained by inter-frame coding is reversibly embedded in the translated image for nearly lossless source image reconstruction. Extensive experimental results and analysis demonstrate that the proposed scheme can achieve a high level of performance in image quality and security.

关键词： image-to-image translation generative adversarial network inter-frame coding reversible data embedding

来源：评论

学校读者我要写书评

暂无评论

SPCNet: A Panoramic image depth estimation method based on spherical convolution

SPCNet: A Panoramic image depth estimation method based on s...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： He, Si Liu, Yu Wang, Yumei Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Res Ctr Networks & Commun Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781728185514

As an emerging media format, virtual reality (VR) has attracted the attention of researchers. 6-DoF VR can reconstruct the surrounding environment with the help of the depth information of the scene, so as to provide users with immersive experience. However, due to the lack of depth information in panoramic image, it is still a challenge to convert panorama to 6-DOF VR. In this paper, we propose a new depth estimation method SPCNet based on spherical convolution to solve the problem of depth information restoration of panoramic image. Particularly, spherical convolution is introduced to improve depth estimation accuracy by reducing distortion, which is attributed to Equi-Rectangular Projection (ERP). The experimental results show that many indicators of SPCNet are better than other advanced networks. For example, RMSE is 0.419 lower than UResNet. Moreover, the threshold accuracy of depth estimation has also been improved.

关键词： Depth estimation Spherical convolution Panoramic image Virtual reality

来源：评论

学校读者我要写书评

暂无评论

DIRECT: Discrete image Rescaling with Enhancement from Case-specific Textures

DIRECT: Discrete Image Rescaling with Enhancement from Case-...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Yan-An Chen Ching-Chun Hsiao Wen-Hsiao Peng Ching-Chun Huang Natl Yang Ming Chiao Tung Univ Inst Comp Sci & Engn Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781728185514

This paper addresses image resealing, the task of which is to downscale an input image followed by upscaling for the purposes of transmission, storage, or playback on heterogeneous devices. The state-of-the-art image resealing network (known as IRN) tackles image downscaling and upscaling as mutually invertible tasks using invertible affine coupling layers. In particular, for upscaling, IRN models the missing high-frequency component by an input-independent (case-agnostic) Gaussian noise. In this work, we take one step further to predict a case-specific high-frequency component from textures embedded in the downscaled image. Moreover, we adopt integer coupling layers to avoid quantizing the downscaled image. When tested on commonly used datasets, the proposed method, termed DIRECT, improves high-resolution reconstruction quality both subjectively and objectively, while maintaining visually pleasing downscaled images.

关键词： Single image Resealing Discrete Invertible Neural Network Case-specific network

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Blind image Super-Resolution using Iterative Networks

Deep Learning-Based Blind Image Super-Resolution using Itera...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Yaar, Asfand Ates, Hasan F. Gunturk, Bahadir K. Istanbul Medipol Univ Sch Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9781728185514

Deep learning-based single image super-resolution (SR) consistently shows superior performance compared to the traditional SR methods. However, most of these methods assume that the blur kernel used to generate the low-resolution (LR) image is known and fixed (e.g. bicubic). Since blur kernels involved in real-life scenarios are complex and unknown, performance of these SR methods is greatly reduced for real blurry images. Reconstruction of high-resolution (HR) images from randomly blurred and noisy LR images remains a challenging task. Typical blind SR approaches involve two sequential stages: i) kernel estimation;ii) SR image reconstruction based on estimated kernel. However, due to the ill-posed nature of this problem, an iterative refinement could be beneficial for both kernel and SR image estimate. With this observation, in this paper, we propose an image SR method based on deep learning with iterative kernel estimation and image reconstruction. Simulation results show that the proposed method outperforms state-of-the-art in blind image SR and produces visually superior results as well.

关键词： Super-resolution Kernel estimation Blind Iterative Deep learning

来源：评论

学校读者我要写书评

暂无评论

An Error Self-learning Semi-supervised Method for No-reference image Quality Assessment

An Error Self-learning Semi-supervised Method for No-referen...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Feng, Yingjie Li, Sumei Hao, Sihan Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

In recent years, deep learning has achieved significant progress in many respects. However, unlike other research fields with millions of labeled data such as image recognition, only several thousand labeled images are available in image quality assessment (IQA) field for deep learning, which heavily hinders the development and application for IQA. To tackle this problem, in this paper, we proposed an error self-learning semi-supervised method for no-reference (NR) IQA (ESSIQA), which is based on deep learning. We employed an advanced full reference (FR) IQA method to expand databases and supervise the training of network. In addition, the network outputs of expanding images were used as proxy labels replacing errors between subjective scores and objective scores to achieve error self-learning. Two weights of error back propagation were designed to reduce the impact of inaccurate outputs. The experimental results show that the proposed method yielded comparative effect.

关键词： error self-learning semi-supervised no-reference image quality assessment

来源：评论

学校读者我要写书评

暂无评论

Nanopore Sequencing Simulator for DNA Data Storage

Nanopore Sequencing Simulator for DNA Data Storage

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： San Antonio, Eva Gil Heinis, Thomas Carteron, Louis Dimopoulou, Melpomeni Antonini, Marc Univ Cote Azur CNRS Lab I3S UMR 7271 Sophia Antipolis France Imperial Coll London Dept Comp London England

ISBN: (纸本)9781728185514

The exponential increase of digital data and the limited capacity of current storage devices have made clear the need for exploring new storage solutions. Thanks to its biological properties, DNA has proven to be a potential candidate for this task, allowing the storage of information at a high density for hundreds or even thousands of years. With the release of nanopore sequencing technologies, DNA data storage is one step closer to become a reality. Many works have proposed solutions for the simulation of this sequencing step, aiming to ease the development of algorithms addressing nanopore-sequenced reads. However, these simulators target the sequencing of complete genomes, whose characteristics differ from the ones of synthetic DNA. This work presents a nanopore sequencing simulator targeting synthetic DNA on the context of DNA data storage.

关键词： DNA data storage nanopore sequencing sequencing simulator image coding

来源：评论

学校读者我要写书评

暂无评论

DCTResNet: Transform Domain image Deblocking for Motion Blur images

DCTResNet: Transform Domain Image Deblocking for Motion Blur...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Maharjan, Paras Xu, Ning Xu, Xuan Song, Yuyan Li, Zhu Univ Missouri Kansas City MO 64110 USA Kwai Inc Palo Alto CA USA

ISBN: (纸本)9781728185514

Pixel recovery with deep learning has shown to be very effective for a variety of low-level vision tasks like image super-resolution, denoising, and deblurring. Most existing works operate in the spatial domain, and there are few works that exploit the transform domain for image restoration tasks. In this paper, we present a transform domain approach for image deblocking using a deep neural network called DCTResNet. Our application is compressed video motion deblur, where the input video frame has blocking artifacts that make the deblurring task very challenging. Specifically, we use a block-wise Discrete Cosine Transform (DCT) to decompose the image into its low and high-frequency sub-band images and exploit the strong subband specific features for more effective deblocking solutions. Since JPEG also uses DCT for image compression, using DCT sub-band images for image deblocking helps to learn the JPEG compression prior to effectively correct the blocking artifacts. Our experimental results show that both PSNR and SSIM for DCTResNet perform more favorably than other state-of-the-art (SOTA) methods, while significantly faster in inference time.

关键词： Discrete cosine transforms

来源：评论

学校读者我要写书评

暂无评论

Two Stage image Restoration based on Histogram Equalization and Edge Computation for image 1

Two Stage Image Restoration based on Histogram Equalization ...

引用

1st International conference on Computer, Power and communications, ICCPC 2022

作者： Kumar, V. Senthil Jayalakshmi, V. Vels Institute of Science Technology and Advanced Studies Dept. of Computer Science Chennai India

ISBN: (纸本)9798350397840

image restoration is the basic technology in image processing to increase the contrast and brightness of the image. Also, this technique should help to characterize the content of an image with high efficiency. It is widely used in many applications. This paper introduces the potential of the two stages functional in an image for image restoration. In the first stage, pair wise pixel histogram equalization is introduced to enhance the image content. For the betterment in the visual quality of foreground region present in the image, the image are analysed by finding edges of the object in the second stage. Also, markov chain transition is used for efficient edge detection. © 2022 IEEE.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共499页 << < 27 28 29 30 31 32 33 34 35 36 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：