检索结果-内蒙古大学图书馆

Revisiting convolutional sparse coding for Image Denoising: From a Multi-Scale Perspective

IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 1202-1206页

作者： Xu, Jingyi Deng, Xin Xu, Mai Beihang Univ Sch Cyber Sci & Technol Beijing 100190 Peoples R China Beihang Univ Dept Elect Informat Engn Beijing 100190 Peoples R China

Recently, convolutional sparse coding (CSC) has shown great success in many image processing tasks, such as image super-resolution and image separation. However, it performs poorly in image denoising task. In this letter, we provide a new insight for CSC denoising by revisiting the CSC from a multi-scale perspective. We propose a multi-scale CSC model for image denoising. By unrolling the multi-scale solution into a learnable network, we obtain an interpretable lightweight multi-scale network, namely MCSCNet. Experimental results show that the proposed MCSCNet significantly advances the denoising performance, with an average PSNR improvement of 0.32 dB over the state-of-the-art (SOTA) CSC based method. In addition, our MCSCNet is on par with many SOTA deep learning based methods, with less network parameters and lower FLOPs. The ablation study also validates the effectiveness of the multi-scale CSC mechanism.

关键词： Image denoising Dictionaries convolutional codes Task analysis Convolution Image reconstruction Mathematical models convolutional sparse coding image denoising

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Transfer Learning via Multi-Scale convolutional sparse coding for Biomedical Applications

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2018年第5期40卷 1182-1194页

作者： Chang, Hang Han, Ju Zhong, Cheng Snijders, Antoine M. Mao, Jian-Hua Lawrence Berkeley Natl Lab Berkeley Biomed Data Sci Ctr Biol Syst & Engn Div Berkeley CA 94720 USA

The capabilities of (I) learning transferable knowledge across domains;and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns;and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains.

关键词： Transfer learning sharable information convolutional sparse coding deep learning biomedical application brain tumors low dose ionizing radiation (LDIR) mouse model breast cancer subtypes

来源：评论

学校读者我要写书评

暂无评论

Image Reconstruction via Manifold Constrained convolutional sparse coding for Image Sets

引用

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2017年第7期11卷 1072-1081页

作者： Yang, Linlin Li, Ce Han, Jungong Chen, Chen Ye, Qixiang Zhang, Baochang Cao, Xianbin Liu, Wanquan Beihang Univ Dept Automat Sci & Elect Engn Beijing 100191 Peoples R China China Univ Min & Technol Beijing 100083 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4WA England Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Cent Florida Orlando FL 32816 USA Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Curtin Univ Perth WA 6845 Australia

Convolution sparse coding (CSC) has attracted much attention recently due to its advantages in image reconstruction and enhancement. However, the coding process suffers from perturbations caused by variations of input samples, as the consistence of features from similar input samples are not well addressed in the existing literature. In this paper, we will tackle this feature consistence problem from a set of samples via a proposed manifold constrained convolutional sparse coding (MCSC) method. The core idea of MCSC is to use the intrinsic manifold (Laplacian) structure of the input data to regularize the traditional CSC such that the consistence between features extracted from input samples can be well preserved. To implement the proposed MCSC method efficiently, the alternating direction method of multipliers (ADMM) approach is employed, which can consistently integrate the underlying Laplacian constraints during the optimization process. With this regularized data structure constraint, the MCSC can achieve a much better solution which is robust to the variance of the input samples against overcomplete filters. We demonstrate the capacity of MCSC by providing the state-of-the-art results when applied it to the task of reconstructing light fields. Finally, we show that the proposed MCSC is a generic approach as it also achieves better results than the state-of-the-art approaches based on convolutional sparse coding in other image reconstruction tasks, such as face reconstruction, digit reconstruction, and image restoration.

关键词： convolutional sparse coding image reconstruction image deblurring light field manifold constrained convolutional sparse coding

来源：评论

学校读者我要写书评

暂无评论

Image classification via convolutional sparse coding

引用

VISUAL COMPUTER 2023年第5期39卷 1731-1744页

作者： Nozaripour, Ali Soltanizadeh, Hadi Semnan Univ Dept Elect Comp Engn Semnan *** Iran Semnan Univ Fac Elect Comp Engn Semnan *** Iran

The convolutional sparse coding (CSC) model has recently attracted a lot of attention in the signal and image processing communities. Since, in traditional sparse coding methods, a significant assumption is that all input samples are independent, so it is not well for most dependent works. In such cases, CSC models are a good choice. In this paper, we proposed a novel CSC-based classification model which combines the local block coordinate descent (LoBCoD) algorithm with the classification strategy. For this, in the training phase, the convolutional dictionary atoms (filters) of each class are learned by all training samples of the same class. In the test phase, the label of the query sample can be determined based on the reconstruction error of the filters related to every subject. Experimental results on five benchmark databases at the different number of training samples clearly demonstrate the superiority of our method to many state-of-the-art classification methods. Besides, we have shown that our method is less dependent on the number of training samples and therefore it can better work than other methods in small databases with fewer samples. For instance, increases of 26.27%, 18.32%, 11.35%, 13.5%, and 19.3% in recognition rates are observed for our method when compared to conventional SRC for five used databases at the least number of training samples per class.

关键词： convolutional sparse coding sparse representation Image classification Filters Feature map

来源：评论

学校读者我要写书评

暂无评论

Context-Dependent Piano Music Transcription With convolutional sparse coding

引用

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2016年第12期24卷 2218-2230页

作者： Cogliati, Andrea Duan, Zhiyao Wohlberg, Brendt Univ Rochester Dept Elect & Comp Engn 601 Elmwood Ave Rochester NY 14627 USA Los Alamos Natl Lab Div Theoret Los Alamos NM 87545 USA

This paper presents a novel approach to automatic transcription of piano music in a context-dependent setting. This approach employs convolutional sparse coding to approximate the music waveform as the summation of piano note waveforms (dictionary elements) convolved with their temporal activations (onset transcription). The piano note waveforms are pre-recorded for the specific piano to be transcribed in the specific environment. During transcription, the note waveforms are fixed and their temporal activations are estimated and post-processed to obtain the pitch and onset transcription. This approach works in the time domain, models temporal evolution of piano notes, and estimates pitches and onsets simultaneously in the same framework. Experiments show that it significantly outperforms a state-of-the-art music transcriptionmethod trained in the same context-dependent setting, in both transcription accuracy and time precision, in various scenarios including synthetic, anechoic, noisy, and reverberant environments.

关键词： Automatic music transcription convolutional sparse coding piano transcription reverberation

来源：评论

学校读者我要写书评

暂无评论

Generalized convolutional sparse coding With Unknown Noise

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 5386-5395页

作者： Wang, Yaqing Kwok, James T. Ni, Lionel M. Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Baidu Res Business Intelligence Lab Beijing 100085 Peoples R China Natl Engn Lab Deep Learning Technol & Applicat Beijing 100085 Peoples R China

convolutional sparse coding (CSC) can learn representative shift-invariant patterns from data. However, existing CSC methods assume the Gaussian noise, which can be restrictive in some challenging applications. In this paper, we propose a generalized CSC model capable of handling complicated unknown noise. The noise is modeled by the Gaussian mixture model, which can approximate any continuous probability density function. The Expectation-Maximization algorithm is used to solve the resultant learning problem. For efficient optimization, the crux is to speed up the convolution in the frequency domain while keeping the other computations involving the weight matrix in the spatial domain. We design an efficient solver for the weighted CSC problem in the M-step. The dictionary and codes are updated simultaneously by an efficient nonconvex accelerated proximal gradient algorithm. The resultant procedure, called generalized convolutional sparse coding (GCSC), obtains the same space complexity and a smaller running time than existing CSC methods (which are limited to the Gaussian noise). Extensive experiments on synthetic and real-world noisy data sets validate that GCSC can model the noise effectively and obtain high-quality filters and representations.

关键词： convolutional sparse coding noise modeling Gaussian mixture model

来源：评论

学校读者我要写书评

暂无评论

Working Locally Thinking Globally: Theoretical Guarantees for convolutional sparse coding

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2017年第21期65卷 5687-5701页

作者： Papyan, Vardan Sulam, Jeremias Elad, Michael Technion Israel Inst Technol Dept Comp Sci IL-3200003 Haifa Israel

The celebrated sparse representation model has led to remarkable results in various signal processing tasks in the last decade. However, despite its initial purpose of serving as a global prior for entire signals, it has been commonly used for modeling low dimensional patches due to the computational constraints it entails when deployed with learned dictionaries. A way around this problem has been recently proposed, adopting a convolutional sparse representation model. This approach assumes that the global dictionary is a concatenation of banded circulant matrices. While several works have presented algorithmic solutions to the global pursuit problem under this new model, very few truly-effective guarantees are known for the success of such methods. In this paper, we address the theoretical aspects of the convolutional sparse model providing the first meaningful answers to questions of uniqueness of solutions and success of pursuit algorithms, both greedy and convex relaxations, in ideal and noisy regimes. To this end, we generalize mathematical quantities, such as the l(0) norm, mutual coherence, Spark and restricted isometry property to their counterparts in the convolutional setting, intrinsically capturing local measures of the global model. On the algorithmic side, we demonstrate how to solve the global pursuit problem by using simple local processing, thus offering a first of its kind bridge between global modeling of signals and their patch-based local treatment.

关键词： sparse representations convolutional sparse coding uniqueness guarantees stability guarantees orthogonal matching pursuit basis pursuit global modeling local processing

来源：评论

学校读者我要写书评

暂无评论

Interpretable Multi-Modal Image Registration Network Based on Disentangled convolutional sparse coding

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 1078-1091页

作者： Deng, Xin Liu, Enpeng Li, Shengxi Duan, Yiping Xu, Mai Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Multi-modal image registration aims to spatially align two images from different modalities to make their feature points match with each other. Captured by different sensors, the images from different modalities often contain many distinct features, which makes it challenging to find their accurate correspondences. With the success of deep learning, many deep networks have been proposed to align multi-modal images, however, they are mostly lack of interpretability. In this paper, we first model the multi-modal image registration problem as a disentangled convolutional sparse coding (DCSC) model. In this model, the multi-modal features that are responsible for alignment (RA features) are well separated from the features that are not responsible for alignment (nRA features). By only allowing the RA features to participate in the deformation field prediction, we can eliminate the interference of the nRA features to improve the registration accuracy and efficiency. The optimization process of the DCSC model to separate the RA and nRA features is then turned into a deep network, namely Interpretable Multi-modal Image Registration Network (InMIR-Net). To ensure the accurate separation of RA and nRA features, we further design an accompanying guidance network (AG-Net) to supervise the extraction of RA features in InMIR-Net. The advantage of InMIR-Net is that it provides a universal framework to tackle both rigid and non-rigid multi-modal image registration tasks. Extensive experimental results verify the effectiveness of our method on both rigid and non-rigid registrations on various multi-modal image datasets, including RGB/depth images, RGB/near-infrared (NIR) images, RGB/multi-spectral images, T1/T2 weighted magnetic resonance (MR) images and computed tomography (CT)/MR images. The codes are available at https://***/lep990816/Interpretable-Multi-modal-Image-Registration.

关键词： Image registration Task analysis Measurement convolutional codes Strain Image coding Generative adversarial networks Multi-modal image registration convolutional sparse coding interpretable network

来源：评论

学校读者我要写书评

暂无评论

Penalized-Likelihood PET Image Reconstruction Using 3D Structural convolutional sparse coding

引用

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2022年第1期69卷 4-14页

作者： Xie, Nuobei Gong, Kuang Guo, Ning Qin, Zhixing Wu, Zhifang Liu, Huafeng Li, Quanzheng Zhejiang Univ Coll Opt Sci & Engn State Key Lab Modern Opt Instrumentat Hangzhou 310000 Peoples R China Massachusetts Gen Hosp Dept Radiol Boston MA 02114 USA Harvard Med Sch Boston MA 02114 USA Shanxi Med Univ Hosp 1 Dept Nucl Med Taiyuan Shanxi Peoples R China

Positron emission tomography (PET) is widely used for clinical diagnosis. As PET suffers from low resolution and high noise, numerous efforts try to incorporate anatomical priors into PET image reconstruction, especially with the development of hybrid PET/CT and PET/MRI systems. In this work, we proposed a cube-based 3D structural convolutional sparse coding (CSC) concept for penalized-likelihood PET image reconstruction, named 3D PET-CSC. The proposed 3D PET-CSC takes advantage of the convolutional operation and manages to incorporate anatomical priors without the need of registration or supervised training. As 3D PET-CSC codes the whole 3D PET image, instead of patches, it alleviates the staircase artifacts commonly presented in traditional patch-based sparse coding methods. Compared with traditional coding methods in Fourier domain, the proposed method extends the 3D CSC to a straightforward approach based on the pursuit of localized cubes. Moreover, we developed the residual-image and order-subset mechanisms to further reduce the computational cost and accelerate the convergence for the proposed 3D PET-CSC method. Experiments based on computer simulations and clinical datasets demonstrate the superiority of 3D PET-CSC compared with other reference methods.

关键词： Three-dimensional displays Image reconstruction Positron emission tomography convolutional codes Convolution Encoding Image coding Positron emission tomography convolutional sparse coding 3D image reconstruction anatomical prior multi-modality

来源：评论

学校读者我要写书评

暂无评论

Variations on the convolutional sparse coding Model

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 519-528页

作者： Rey-Otero, Ives Sulam, Jeremias Elad, Michael Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

Over the past decade, the celebrated sparse representation model has achieved impressive results in various signal and image processing tasks. A convolutional version of this model, termed convolutional sparse coding (CSC), has been recently reintroduced and extensively studied. CSC brings a natural remedy to the limitation of typical sparse enforcing approaches of handling global and high-dimensional signals by local, patch-based, processing. While the classic field of sparse representations has been able to cater for the diverse challenges of different signal processing tasks by considering a wide range of problem formulations, almost all available algorithms that deploy the CSC model consider the same problem form. As we argue in this paper, this CSC pursuit formulation is also too restrictive as it fails to explicitly exploit some local characteristics of the signal. This work expands the range of formulations for the CSC model by proposing two convex alternatives that merge global norms with local penalties and constraints. The main contribution of this work is the derivation of efficient and provably converging algorithms to solve these new sparse coding formulations.

关键词： sparse representation convolutional sparse coding parallel proximal algorithm convex optimization

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：