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检索条件"主题词=Convolutional Sparse Coding"

共 168 条记录，以下是51-60 订阅

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Simultaneous image deblurring and inpainting via convolutional sparse coding 8

Simultaneous image deblurring and inpainting via Convolution...

引用

Conference on Optoelectronic Imaging and Multimedia Technology VIII

作者： Wang, Shengbiao Chen, Bolun Chen, Huasong Gao, Shixuan Wang, Junhao Feng, Qiansheng Ma, Tianlong Huaiyin Inst Technol Fac Comp & Software Engn Huaian Peoples R China Huaiyin Inst Technol Fac Math & Phys Huaian Peoples R China

ISBN: (数字)9781510646445

ISBN: (纸本)9781510646445;9781510646438

Image deblurring and inpainting are traditional image processing problems, and the effects achieved for high-resolution images are not satisfactory. In recent years, convolutional sparse coding (CSC) has been received more attention and introduced into image processing, such as blind deblurring. However, none of the works address the issue containing both blur and inpainting In this work, we propose a novel framework of CSC for simultaneous image deblurring and inpainting First, we learn a dictionary instead of applying a given dictionary for better image representation. Second, we use the learned dictionary with the l(1) norm to regularize images. In addition, we apply a total anisotropic variation to enhance the edges of the image. Usually, we use the alternating direction method of multipliers (ADMM) formulation in the Fourier domain for the dictionary. We demonstrate the proposed training scheme for simultaneous image deblurring and inpainting, achieving state-of-the-art results.

关键词： Image Deblurring and Inpainting convolutional sparse coding Dictionary Learning ADMM

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PROJECTING ONTO THE MULTI-LAYER convolutional sparse coding MODEL

PROJECTING ONTO THE MULTI-LAYER CONVOLUTIONAL SPARSE CODING ...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Sulam, Jeremias Papyan, Vardan Romano, Yaniv Elad, Michael Technion Israel Inst Technol Comp Sci Dept Haifa Israel Technion Israel Inst Technol Elect Engn Dept Haifa Israel

ISBN: (纸本)9781538646588

The recently proposed Multi-Layer convolutional sparse coding (ML-CSC) model, consisting of a cascade of convolutional sparse layers, provides a new interpretation of convolutional Neural Networks (CNNs). Under this framework, the forward pass in a CNN is equivalent to an algorithm that estimates nested sparse representation vectors from a given input signal. Despite having served as a pivotal connection between CNNs and sparse modeling, it is still unclear how to develop pursuit algorithms that serve this model exactly. In this work, we propose a new pursuit formulation by adopting a projection approach. We provide new and improved bounds on the stability of the resulting convolutional sparse representations, and we propose a multi-layer projection algorithm to retrieve them. We demonstrate this algorithm numerically, showing that it is superior to the Layered Basis Pursuit alternative in retrieving the representations of signals belonging to the ML-CSC model.

关键词： convolutional sparse coding Multi-layer Pursuit Stability Guarantees convolutional Neural Networks

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Spectral Video Compression Using convolutional sparse coding

Spectral Video Compression Using Convolutional Sparse Coding

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Data Compression Conference (DCC)

作者： Barajas-Solano, C. Ramirez, J. M. Arguello, H. Univ Ind Santander Bucaramanga Colombia Univ Rey Juan Carlos Mostoles Spain

ISBN: (纸本)9781728164571

Spectral Videos (SV) are datasets containing spatial-spectral-and -temporal information of a moving scene and this kind of information have been successfully used in medicine, remote sensing, and military application. However, expensive acquisition processes and difficulties in equipment manufacture lead to low-resolution datasets. Therefore, super-resolution (SR) techniques have emerged as a processing tool that recovers a high-resolution dataset by expressing the measurements as compressed versions of the desired data. Furthermore, the convolutional sparse coding (CSC) has been developed as a signal model that learns a dictionary directly from the target signal, improving the reconstruction quality. This work proposes to extend the CSC formulation to consider temporal correlations in SVs, exploiting the shifting invariance property of the CSC model. The simulation results show a PSNR improvement in up to 2.5dB with respect to the state-of-the-art methods, preserving the edges and textures of the spectral video frames.

关键词： spectral video convolutional sparse coding data compression

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RANDOM GENERATED DICTIONARIES FOR convolutional sparse coding: AN ELM INTERPRETATION FOR SIMPLE CSC APPLICATIONS 29

RANDOM GENERATED DICTIONARIES FOR CONVOLUTIONAL SPARSE CODIN...

引用

IEEE International Conference on Image Processing (ICIP)

作者： Rodriguez, Paul Pontificia Univ Catolica Peru Elect Dept Lima Peru

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The most basic ELM (extreme learning machines) architecture consists on a single-hidden layer feedforward neural network, with random input weights, plus a densely connected output layer whose weights must be learned. Among other interpretations, it can be understood as using an untrained dictionary (with random entries) along with a non-linear activation function to obtain a representation. Compared to Neural Networks (NN) or convolutional NN (CNN), ELM is very fast to train. Inspired by the ELM methodology, in this paper we explore the usefulness of using a randomly generated filterbank (FB) as the convolutional dictionary in convolutional sparse coding (CSC) representations and assess its performance for simple applications such denoising and super resolution, when compared to learned FBs. Our main conclusions are that a randomly generated FB (i) has a competitive (restoration) performance when compared to a learned FB, (ii) its performance depends on the actual distribution of its values, i.e. Gaussian, uniform, lognormal, etc., and problem, and (iii) it may ease or potentially eliminate the need for the CDL (convolutional dictionary learning) step in CSR's applications.

关键词： convolutional sparse coding extreme learning machines

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Distributed compressed video sensing based on convolutional sparse coding

Distributed compressed video sensing based on convolutional ...

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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

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FAST convolutional sparse coding WITH SEPARABLE FILTERS

FAST CONVOLUTIONAL SPARSE CODING WITH SEPARABLE FILTERS

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Silva, Gustavo Quesada, Jorge Rodriguez, Paul Wohlberg, Brendt Pontificia Univ Catolica Peru Dept Elect Engn Lima Peru Los Alamos Natl Lab Appl Math & Plasma Phys T5 Los Alamos NM 87545 USA

ISBN: (纸本)9781509041176

convolutional sparse representations (CSR) of images are receiving increasing attention as an alternative to the usual independent patch-wise application of standard sparse representations. For CSR the dictionary is a filter bank of non-separable 2D filters, and the representation itself can be viewed as the synthesis dual of the analysis representation provided by a single level of a convolutional neural network (CNN). The current state-of-the-art convolutional sparse coding (CSC) algorithms achieve their computational efficiency by applying the convolutions in the frequency domain. It has been shown that any given 2D non-separable filter bank can be approximated as a linear combination of a relatively small number of separable filters. This approximation has been exploited for computationally efficient CNN implementations, but has thus far not been considered for convolutional sparse coding. In this paper we propose a computationally efficient algorithm, that apply the convolution in the spatial domain, to solve the CSC problem when the corresponding dictionary filters are separable. Our algorithm, based on the ISTA framework, use a two-term penalty function to attain competitive results when compared to the state-of-the-art methods in terms of computational performance, sparsity and reconstruction quality.

关键词： convolutional sparse Representation convolutional sparse coding Separable Filters

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PIANO MUSIC TRANSCRIPTION WITH FAST convolutional sparse coding 25

PIANO MUSIC TRANSCRIPTION WITH FAST CONVOLUTIONAL SPARSE COD...

引用

IEEE International Workshop on Machine Learning for Signal Processing

作者： Cogliati, Andrea Duan, Zhiyao Wohlberg, Brendt Univ Rochester Rochester NY USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781467374545

Automatic music transcription (AMT) is the process of converting an acoustic musical signal into a symbolic musical representation, such as a MIDI file, which contains the pitches, the onsets and offsets of the notes and, possibly, their dynamics and sources (i.e., instruments). Most existing algorithms for AMT operate in the frequency domain, which introduces the well known time/frequency resolution trade-off of the Short Time Fourier Transform and its variants. In this paper, we propose a time-domain transcription algorithm based on an efficient convolutional sparse coding algorithm in an instrumentspecific scenario, i.e., the dictionary is trained and tested on the same piano. The proposed method outperforms a current state-of-the-art AMT method by over 26% in F-measure, achieving a median Fmeasure of 93.6%, and drastically increases both time and frequency resolutions, especially for the lowest octaves of the piano keyboard.

关键词： Automatic Music Transcription convolutional sparse coding Shift Invariant sparse Representation

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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

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Single Image Super-resolution Based on Residual Learning and convolutional sparse coding 10

Single Image Super-resolution Based on Residual Learning and...

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10th International Conference on Digital Image Processing (ICDIP)

作者： Xie, Chao Jiang, Shengqin Lu, Xiaobo Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

In this paper, we propose an improved method for single image super-resolution on the basis of residual learning and convolutional sparse coding (CSC). The key idea of it is to first perform a CSC-based decomposition on the input so that it can be split into two predefined parts: the smooth and residual components. Then, the extracted components are individually mapped according to their own characteristics, rather than directly perform a mapping from the original input. Specifically, we place more emphases on the residual one as it is much important to our task, while the smooth one is just propagated to the final output for providing a quick reference. Accordingly, the final architecture of our method conceptually integrates all the above steps into a completely end-to-end trainable deep network. Extensive experimental results indicate that our proposed method outperforms many state-of-the-art methods in terms of both visual fidelity and objective evaluation.

关键词： Single image super-resolution residual learning deep convolutional neural networks convolutional sparse coding

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Weakly Supervised Multi-Needle Detection in 3D Ultrasound Images with Bidirectional convolutional sparse coding

Weakly Supervised Multi-Needle Detection in 3D Ultrasound Im...

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SPIE Medical Imaging Meeting - Ultrasonic Imaging and Tomography

作者： Zhang, Yupei Harms, Joseph Lei, Yang Wang, Tonghe Liu, Tian Jani, Ashesh B. Curran, Walter J. Patel, Pretesh Yang, Xiaofeng Emory Univ Dept Radiat Oncol Atlanta GA 30322 USA Emory Univ Winship Canc Inst Atlanta GA 30322 USA

ISBN: (纸本)9781510634060

Accurate and automatic multi-needle detection in three-dimensional (3D) ultrasound (US) is a key step of treatment planning for US-guided prostate high dose rate (HDR) brachytherapy. In this paper, we propose a workflow for multi-needle detection in 3D ultrasound (US) images with corresponding CT images used for supervision. Since the CT images do not exactly match US images, we propose a novel sparse model, dubbed Bidirectional convolutional sparse coding (BiCSC), to tackle this weakly supervised problem. BiCSC aims to extract the latent features from US and CT and then formulate a relationship between them where the learned features from US yield to the features from CT. Resultant images allow for clear visualization of the needle while reducing image noise and artifacts. On the reconstructed US images, a clustering algorithm is employed to find the cluster centers which correspond to the true needle position. Finally, the random sample consensus algorithm (RANSAC) is used to model a needle per ROI. Experiments are conducted on prostate image datasets from 10 patients. Visualization and quantitative results show the efficacy of our proposed workflow. This learning-based technique could provide accurate needle detection for US-guided high-dose-rate prostate brachytherapy, and further enhance the clinical workflow for prostate HDR brachytherapy.

关键词： Multi-needle detection 3D US images weakly supervised method convolutional sparse coding RANSAC prostate cancer brachytherapy needle tip detection needle visualization

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