检索结果-内蒙古大学图书馆

convolutional sparse coding for RGB plus NIR Imaging

IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第4期27卷 1611-1625页

作者： Hu, Xuemei Heide, Felix Dai, Qionghai Wetzstein, Gordon Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Stanford Univ Dept Elect Engn Stanford CA 94306 USA Zhejiang Future Technol Inst Jiaxing 314006 Peoples R China

Emerging sensor designs increasingly rely on novel color filter arrays (CFAs) to sample the incident spectrum in unconventional ways. In particular, capturing a near-infrared (NIR) channel along with conventional RGB color is an exciting new imaging modality. RGB+NIR sensing has broad applications in computational photography, such as low-light denoising, it has applications in computer vision, such as facial recognition and tracking, and it paves the way toward low-cost single-sensor RGB and depth imaging using structured illumination. However, cost-effective commercial CFAs suffer from severe spectral cross talk. This cross talk represents a major challenge in high-quality RGB+NIR imaging, rendering existing spatially multiplexed sensor designs impractical. In this work, we introduce a new approach to RGB+NIR image reconstruction using learned convolutional sparse priors. We demonstrate high-quality color and NIR imaging for challenging scenes, even including high-frequency structured NIR illumination. The effectiveness of the proposed method is validated on a large data set of experimental captures, and simulated benchmark results which demonstrate that this work achieves unprecedented reconstruction quality.

关键词： Computational photography convolutional sparse coding structured illumination

来源：评论

学校读者我要写书评

暂无评论

Distributed compressed video sensing based on convolutional sparse coding

Distributed compressed video sensing based on convolutional ...

引用

International Symposium on Intelligent Signal Processing and Communication Systems (lSPACS)

作者： Mizokami, Tomohito Kuroki, Yoshimitsu Kurume Coll Natinal Inst Technol KOSEN Fukuoka Japan

ISBN: (纸本)9781728130385

This paper discusses a Distributed Compressed Video Sensing (DCVS) framework using convolutional sparse coding (CSC). CSC is a technique to represent a signal as convolutions of filters and corresponding coefficients. Conventional block based DCVS methods divide a given video sequence into key and non-key frames. The key frames are decoded independently like still images, and the non-key frames use Side Information (SI) generated with previously decoded key frames. The sparse dictionaries of the non-key frames are designed with the SIs. However, in CSC based methods, a non-key frame can use the dictionary of the nearest key frame in the temporal domain since the dictionary filters, namely features, are robuster against motions than those of block based methods.

关键词： distributed compressed video sensing dictionary learning convolutional sparse coding

来源：评论

学校读者我要写书评

暂无评论

LEARNED MULTIMODAL convolutional sparse coding FOR GUIDED IMAGE SUPER-RESOLUTION 26

LEARNED MULTIMODAL CONVOLUTIONAL SPARSE CODING FOR GUIDED IM...

引用

26th IEEE International Conference on Image Processing (ICIP)

作者： Marivani, Iman Tsiligianni, Evaggelia Cornelis, Bruno Deligiannis, Nikos Vrije Univ Brussel Pl Laan 2 B-1050 Brussels Belgium IMEC Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (纸本)9781538662496

The success of deep learning in various tasks, including solving inverse problems, has triggered the need for designing deep neural networks that incorporate domain knowledge. In this paper, we design a multimodal deep learning architecture for guided image super-resolution, which refers to the problem of super-resolving a low-resolution image with the aid of a high-resolution image of another modality. The proposed architecture is based on a novel deep learning model, obtained by unfolding a proximal method that solves the problem of convolutional sparse coding with side information. We applied the proposed architecture to super-resolve near-infrared images using RGB images as side information. Experimental results report average PSNR gains of up to 2.85 dB against state-of-the-art multimodal deep learning and sparse coding models.

关键词： Guided image super-resolution convolutional sparse coding multimodal deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Adaptive convolutional sparse coding with Weighted l¹ Norm 38

Adaptive Convolutional Sparse Coding with Weighted l<SUP>1</...

引用

38th Chinese Control Conference (CCC)

作者： Qiu, Jiale Yang, Min Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China

ISBN: (纸本)9789881563972

convolutional sparse coding computes a sparse representation for an entire image by modeling the signal as a sum of several convolutions of dictionary filters and sparse feature maps, instead of the linear combination of dictionary vectors in standard sparse coding. Most of the existing algorithms use a invariant regularization parameter, which can hardly deal well with varying local contrast. In this paper, we propose a new method Adaptive convolutional sparse coding (ACSC) by introducing a set of weight vectors into the l(1) regularization term to set different penalty coefficients for different sparse feature maps, which can better adapt with the varying local sparse requirements of images. Experimental results show that the proposed ACSC is superior to the existing convolutional sparse coding algorithm in image reconstruction.

关键词： sparse coding convolutional sparse coding ADMM Image Reconstruction

来源：评论

学校读者我要写书评

暂无评论

Defect Feature Extraction in Eddy Current Testing Based on convolutional sparse coding

Defect Feature Extraction in Eddy Current Testing Based on C...

引用

IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Tao, Yang Xu, Hanyang Avila, Jorge R. Salas Ktistis, Christos Yin, Wuliang Peyton, Anthony J. Univ Manchester Sch Elect & Elect Engn Manchester M1 3BU Lancs England North Univ China Sch Instrument & Elect Taiyuan 030051 Peoples R China

ISBN: (纸本)9781538634608

In eddy current testing (ECT), feature extraction methods play an important role in the detection and classification of defects. convolutional sparse coding (CSC) has gained attentions in recent years as this can provide a versatile framework in the description of signal and rich options for effective discrimination and classification algorithms. In this paper, we propose a feature extraction method which for the first time applies the CSC model for ECT. The local dictionary in the model is trained using signal segments corresponding to scans of defects. The proposed algorithm has achieved 98% accuracy in terms of the recovery of the location of defect signal segments. The recovered coefficients are adopted as unique features which eventually correspond to the profiles of the defect.

关键词： convolutional sparse coding Eddy Current Testing Feature Extraction

来源：评论

学校读者我要写书评

暂无评论

Adaptive Coded Aperture Design by Motion Estimation using convolutional sparse coding in Compressive Spectral Video Sensing 8

Adaptive Coded Aperture Design by Motion Estimation using Co...

引用

8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： Diaz, N. Noriega-Wandurraga, C. Basarab, A. Tourneret, J-Y Arguelle, H. Univ Ind Santander UIS Dept Elect Engn Bucaramanga 680002 Colombia UIS Dept Math Bucaramanga 680002 Colombia Univ Toulouse IRIT CNRS UMR 5505 F-31062 Toulouse France Univ Toulouse IRIT INP ENSEEIHT TeSA F-31071 Toulouse France UIS Dept Comp Sci Bucaramanga 680002 Colombia

ISBN: (纸本)9781728155494

This paper proposes a new motion estimation method based on convolutional sparse coding to adaptively design the colored-coded apertures in static and dynamic spectral videos. The motion in a spectral video is estimated from a low-resolution reconstruction of the datacube by training a convolutional dictionary per spectral band and solving a minimization problem. Simulations show improvements in terms of peak signal-to-noise ratio (of up to 2 dB) of the reconstructed videos by using the proposed approach, compared with state-of-art non-adaptive coded apertures.

关键词： Compressive spectral video motion estimation adaptive imaging convolutional sparse coding

来源：评论

学校读者我要写书评

暂无评论

sparse-VIEW CT RECONSTRUCTION VIA convolutional sparse coding 16

SPARSE-VIEW CT RECONSTRUCTION VIA CONVOLUTIONAL SPARSE CODIN...

引用

16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Bao, Peng Xia, Wenjun Yang, Kang Zhou, Jiliu Zhang, Yi Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9781538636411

Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To deal with these problems, the convolutional sparse coding (CSC) has been proposed and introduced into various applications. In this paper, inspired by the successful applications of CSC in the field of signal processing, we propose a novel sparse-view CT reconstruction method based on CSC with gradient regularization on feature maps. By directly working on the whole image, which need not to divide the image into overlapped patches like dictionary learning based methods, the proposed method can maintain more details and avoid the artifacts caused by patch aggregation. Experimental results demonstrate that the proposed method has better performance than several existing algorithms in both qualitative and quantitative aspects.

关键词： sparse-view convolutional sparse coding

来源：评论

学校读者我要写书评

暂无评论

Separating Musical Sources With convolutional sparse coding 19

Separating Musical Sources With Convolutional Sparse Coding

引用

2nd International Conference on Applications of Intelligent Systems (APPIS)

作者： Dubey, Mohit Jones, Haydn Thresher, Austin Kenyon, Garrett Oberlin Coll & Conservatory Oberlin OH 44074 USA New Mexico Inst Min & Technol Socorro NM 87801 USA New Mexico Consortium Los Alamos NM USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781450360852

The solution to the problem of separating multiple vocal and instrumental tracks from a single audio waveform is solved naturally by the human auditory cortex but has yet to be effectively implemented computationally. In this paper, we demonstrate a neurally-inspired approach to separating bass, drums, vocals and other instruments from sparse encodings of phase-rich Fourier and Constant-Q representations of stereo musical data. Our sparse encodings are generated from learned features that are both spectrally and temporally convolutional, similar to the hemispheric lateralization of human auditory cortex. We find that learning from neurally inspired Constant-Q representations provides better separation over Fourier spectrograms due to the presence of structure that is convolutional in log-frequency that aids in the differentiation of instruments.

关键词： convolutional sparse coding source separation constant-Q transform

来源：评论

学校读者我要写书评

暂无评论

Respiration and Heartbeat Rates Measurement Based on convolutional sparse coding

Respiration and Heartbeat Rates Measurement Based on Convolu...

引用

IEEE MTT-S International Microwave Biomedical Conference (IMBioC)

作者： Wang, Pengfei Liu, Miao Zhu, Huijie Liang, Fulai Lv, Hao Li, Zhao Wang, Jianqi Air Force Mil Med Univ Dept Mil Biomed Engn Xian 710032 Peoples R China PLA Gen Hosp Northern Theater Command Shenyang 110016 Peoples R China First Engn Sci Res Inst Gen Armaments Dept Wuxi 214035 Jiangsu Peoples R China

ISBN: (纸本)9781538673959

Accurate access to respiration rate (RR) and heartbeat rate (HR) through radar is of great importance in many applications. In this paper, a novel method based on convolutional sparse coding (CSC) is proposed for respiration and heartbeat rates measurement. To solve the problem of algorithm performance degradation caused by insufficient samples, a number of samples are generated by mixing random noise with original signal. Then radar signals are processed by CSC directly in the time domain. The method is tested by a vital sign data generated by finite differences time domain (FDTD) simulation. The results demonstrate that the proposed processing approach can accurately extract the respiration and heartbeat components with the generated data of 5 seconds.

关键词： Respiration rate heartbeat rate convolutional sparse coding FDTD

来源：评论

学校读者我要写书评

暂无评论

Single Image Super-Resolution via convolutional sparse coding with One Group of Filters 2019

Single Image Super-Resolution via Convolutional Sparse Codin...

引用

Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

作者： Mingming Bai Yunjie Zhang BinZhang Department of Mathematics Dalian Maritime University Dalian China

ISBN: (纸本)9781450376273

Using the characteristic that image patches can be represented sparsely on a selected over-complete dictionary, the sparse coding based super-resolution (ScSR) method pioneers a new avenue for the SR technology. However, ScSR will result in obvious block-effect because of the block preprocessing of the image. For this purpose, Zeiler et al. proposed convolutional sparse coding based SR (CSC-SR) method to solve this problem. Because it needs to learn two groups of filters and mapping function between HR and LR feature maps, CSC-SR usually puts forward high demand to storage and relatively large the calculation expense. In this paper, we propose a modified CSC-SR method to single image super-resolution. The proposed method only needs to train one group of filers, and explores directly the mapping function by least squares algorithm, which can effectively reduce the workload of model training. Experimental results for different test images show that our model has achieved effective improvement over the previous methods.

关键词： sparse representation Super-resolution convolutional sparse coding

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：