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Single-image super resolution using evolutionary sparse coding technique

IET IMAGE PROCESSING 2017年第1期11卷 13-21页

作者： Ahmadi, Kaveh Salari, Ezzatollah Univ Toledo Dept Elect Engn & Comp Engn 2801 W Bancroft St Toledo OH 43606 USA

sparse coding (SC) has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. SC applications have been explored in many fields such as image super resolution (SR), image-feature extraction, image reconstruction, and segmentation. In most of these applications, learning-based SC has provided an excellent image quality. SC involves two steps: dictionary construction and searching the dictionary using quadratic programming. This study focuses on the searching step and a new adaptive variation of genetic algorithm is proposed to search and find the optimum closest match in the dictionary. Also, inspired by the proposed evolutionary SC (ESC), a single-image SR algorithm is proposed. A sparse representation for each patch of the low-resolution input image is obtained by ESC and it is used to generate the high-resolution output image. Experimental results show that the proposed ESC-based method would lead to a better SR image quality.

关键词： image coding learning (artificial intelligence) quadratic programming genetic algorithms search problems image resolution evolutionary SC technique sparse coding image processing sparse linear combination over-complete dictionary signal patch representation learning-based SC image quality dictionary construction dictionary search quadratic programming adaptive variation genetic algorithm optimum closest match ESC single-image SR algorithm sparse representation low-resolution input image patch high-resolution output image generation

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Non-negativity and dependence constrained sparse coding for image classification

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年第1期26卷 247-254页

作者： Han, Hong Liu, Sanjun Gan, Lu Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

sparse coding method is a powerful tool in many computer vision applications. However, due to the combinatorial optimization of sparse coding involving both additive and subtractive interactions, the features can cancel each other out with subtraction. And also, in the process of independent coding, the locality and the similarity among the instances which be encoded may be lost. To solve these problems, an image classification framework by leveraging the Non-negative Matrix Factorization and graph Laplacian techniques is presented. Firstly, the Non-negative Matrix Factorization is used to constrain both of the codebook and the corresponding coding coefficients non-negativity. To preserve the dependence properties of the locality and the similarity among the instances, the graph Laplacian regularization is utilized. Then, along with max pooling and spatial pyramid matching, we extend our method to Bag-of-Words image representation. Finally, the linear SVM is leveraged for image classification. Experimental results show that the proposed method achieves or outperforms the state-of-the-art results on several benchmarks. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Non-negative Matrix Factorization Graph Laplacian Dictionary learning sparse coding Spatial pyramid Image classification Parameter selection sparseness

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Towards Robustness and Efficiency of Coherence-Guided Complex Convolutional sparse coding for Interferometric Phase Restoration

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2024年 10卷 690-699页

作者： Ding, Xiang Kang, Jian Bai, Yusong Zhang, Anping Liu, Jialin Yokoya, Naoto Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China Alibaba Grp US DAMO Acad Bellevue WA 98004 USA Univ Tokyo Grad Sch Frontier Sci Tokyo 1138654 Japan RIKEN Ctr Adv Intelligence Project AIP Geoinformat Unit Kashiwa 2778561 Japan

Recently, complex convolutional sparse coding (ComCSC) has demonstrated its effectiveness in interferometric phase restoration, owing to its prominent performance in noise mitigation and detailed phase preservation. By incorporating the estimated coherence into ComCSC as prior knowledge for re-weighting individual complex residues, coherence-guided complex convolutional sparse coding (CoComCSC) further improves the quality of restored phases, especially over heterogeneous land-covers with rapidly varying coherence. However, due to the exploited L-2 norm of the data fidelity term, the original CoComCSC is not robust to outliers when relatively low coherence values are sparsely distributed over high ones. We propose CoComCSC-L1 and CoComCSC-Huber to improve the robustness of CoComCSC based on the L-1 and Huber norms. Moreover, we propose an efficient solver to decrease the computational cost of solving the linear system subproblem within ComCSC-based optimization problems. By comparing the proposed methods to other state-of-the-art methods using both simulated and real data, the proposed methods demonstrate their effectiveness. Additionally, the proposed solver has the potential to improve optimization speed by approximately 10% compared to the state-of-the-art solver.

关键词： Noise Image restoration Filtering Coherence Encoding Convolution Noise reduction SAR SAR interferometry (InSAR) sparse coding Convolutional dictionary learning

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Robust object tracking based on ridge regression and multi-scale local sparse coding

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第1-2期79卷 785-804页

作者： Zhao, Zhiqiang Xiong, Liwen Mei, Zhuolin Wu, Bin Cui, Zongmin Wang, Tianjiang Zhao, Zhijian Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Jiangxi Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Intelligent & Distributed Comp Lab Wuhan 430074 Peoples R China Hunan Univ Sch Business Changsha 410006 Hunan Peoples R China

Recently, the technology of visual object tracking has achieved great success. However, it is still extraordinary challenging for some factors, such as scale variations, partial occlusions and so on. To deal with the problem of scale variations of the target, this paper proposes a hybrid tracking algorithm based on ridge regression and multi-scale local sparse coding. The hybrid tracking algorithm contains three parts. Firstly, a discriminative model based on two ridge regression models which include a correlation filtering ridge regression model and a color statistics ridge regression model, is used to estimate the approximate position of the target. Secondly, a multi-scale local sparse coding with particle filtering model, which combines local overlapped patches and local non-overlapped patches, is used to estimate the precise position and scale variations of the target. Thirdly, the appearance model of the target in the discriminative model based on ridge regression is updated according to the precise position and scale variations of the target in the second part. At the end, extensive experiments verify the effectiveness of the hybrid tracking algorithm in dealing with scale variations of the target.

关键词： Object tracking Ridge regression Correlation filtering sparse coding Particle filtering

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3D CBIR with sparse coding for image-guided neurosurgery

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SIGNAL PROCESSING 2013年第6期93卷 1673-1683页

作者： Qian, Yu Hui, Rui Gao, Xiaohong Middlesex Univ Sch Sci & Technol London NW4 4BT England Gen Navy Hosp Dept Neurosurg Beijing Peoples R China

This research takes an application-specific approach to investigate, extend and implement the state of the art in the fields of both visual information retrieval and machine learning, bridging the gap between theoretical models and real world applications. During an image-guided neurosurgery, path planning remains the foremost and hence the most important step to perform an operation and ensures the maximum resection of an intended target and minimum sacrifice of health tissues. In this investigation, the technique of content-based image retrieval (CBIR) coupled with machine learning algorithms are exploited in designing a computer aided path planning system (CAP) to assist junior doctors in planning surgical paths while sustaining the highest precision. Specifically, after evaluation of approaches of sparse coding and K-means in constructing a codebook, the model of sparse codes of 3D SIFT has been furthered and thereafter employed for retrieving, The novelty of this work lies in the fact that not only the existing algorithms for 2D images have been successfully extended into 3D space, leading to promising results, but also the application of CBIR that is mainly in a research realm, to a clinical sector can be achieved by the integration with machine learning techniques. Comparison with the other four popular existing methods is also conducted, which demonstrates that with the implementation of sparse coding, all methods give better retrieval results than without while constituting the codebook, implying the significant contribution of machine learning techniques. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

关键词： CBIR Computer aided path planning Neurosurgery 3D SIFT sparse coding

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SAR Image Despeckling by Using Nonlocal sparse coding Model

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2018年第7期37卷 3023-3045页

作者： Chen, Shuxuan Gao, Lei Li, Quanyun Beijing Aerosp Automat Control Inst Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing 100854 Peoples R China

Based on the key observation that the coding residuals between the recovered sparse codes of the noisy SAR image and those of the clean SAR image are sparse, we propose a sparse representation-based despeckling algorithm for SAR image. As the sparse codes of the clean SAR image are not available, the rich nonlocal repetitive structures of the logarithmic SAR images are exploited. To collect the similar patches within the logarithmic SAR image, an adaptive similarity evaluation obeying statistical distribution of the logarithmic speckle noise is derived. Experimental results on both synthetic and real SAR images demonstrate the validity of the proposed algorithm.

关键词： sparse coding Nonlocal sparse representation SAR despeckling (denoising)

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Low-Resolution and Low-Quality Face Super-Resolution in Monitoring Scene via Support-Driven sparse coding

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2014年第3期75卷 245-256页

作者： Jiang, Junjun Hu, Ruimin Han, Zhen Wang, Zhongyuan Wuhan Univ Sch Comp Natl Engn Res Ctr Multimedia Software Wuhan 430072 Peoples R China

Monitoring cameras are now widely used to monitor everything from a room in a house to an entire warehouse. However, in real monitoring scenarios, a variety of factors, such as underexposure, optical blurring, defocusing, have an impact on the quality of images, which leads to low-quality and low-resolution (LR) of the individual of interest. Reconstruction of a high-resolution (HR) face image with detailed facial features, from a LR observation based on a set of HR and LR training image pairs, plays an important role in computer vision and face image analysis applications. To super-resolve an HR face given a LR face image, the key issue is how to effectively encode the LR image patch. However, due to stability and accuracy issues, the coding approaches proposed so far are far from satisfactory. In this paper, we present a novel sparse coding method via exploiting the support information on the coding coefficients. According to the distances between the input patch and bases in the dictionary, we first assign different weights to the coding coefficients and then obtain the coding coefficients by solving a weighted sparse problem. Experiments on commonly used databases and some face images on the real monitoring conditions demonstrate that our method outperforms state-of-the-art.

关键词： Intelligent security monitoring Face image Super-resolution sparse coding

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Rate-Distortion Optimized sparse coding With Ordered Dictionary for Image Set Compression

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2018年第12期28卷 3387-3397页

作者： Zhang, Xinfeng Lin, Weisi Zhang, Yabin Wang, Shiqi Ma, Siwei Duan, Lingyu Gao, Wen Nanyang Technol Univ Rapid Rich Object Search Lab Singapore Singapore City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China Peking Univ Inst Digital Media Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

Image set compression has recently emerged as an active research topic due to the rapidly increasing demand in cloud storage. In this paper, we propose a novel framework for image set compression based on the rate-distortion optimized sparse coding. Specifically, given a set of similar images, one representative image is first identified according to the similarity among these images, and a dictionary can be learned subsequently in wavelet domain from the training samples collected from the representative image. In order to improve coding efficiency, the dictionary atoms are reordered according to their use frequencies when representing the representative image. As such, the remaining images can be efficiently compressed with sparse coding based on the reordered dictionary that is highly adaptive to the content of the image set. To further improve the efficiency of sparse coding, the number of dictionary atoms for image patches is further optimized in a rate-distortion sense. Experimental results show that the proposed method can significantly improve the image compression performance compared with JPEG, JPEG2000, and the state-of-the-art dictionary learning-based methods.

关键词： Image set compression sparse coding dictionary learning rate-distortion optimization

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Spatially constrained sparse coding scheme for natural scene categorization

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年 28卷 28-35页

作者： Zhang, Hui Liu, Yi Xie, Bojun Yu, Jian Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China Hebei Univ Coll Math & Comp Sci Key Lab Machine Learning & Computat Intelligence Baoding Hebei Peoples R China

coding and pooling, the major two sequential procedures in sparse coding based scene categorization systems, have drawn much attention in recent years. Yet improvements have been made for coding or pooling separately, this paper proposes a spatially constrained scheme for sparse coding on both steps. Specifically, we employ the m-nearest neighbors of a local feature in the image space to improve the consistency of coding. The benefit is that similar image features will be encoded with similar codewords, which reduced the stochasticity of a conventional coding strategy. We also show that the Viola-Jones algorithm, which is well-known in face detection, can be tailored to learning receptive fields, embedding the spatially constrained information on the pooling step. Extensive experiments on the URIC sport event, 15 natural scenes and the Caltech 101 database suggests that scene categorization performance of several popular algorithms can be ubiquitously improved by incorporating the proposed two spatially constrained sparse coding scheme. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Scene categorization Receptive fields learning Boosting sparse coding k-nearest neighbor Voting Image classification Pooling

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Continuous speech recognition with sparse coding

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COMPUTER SPEECH AND LANGUAGE 2009年第2期23卷 200-219页

作者： Smit, W. J. Barnard, E. Univ Pretoria ZA-0001 Pretoria South Africa Meraka Inst ZA-0001 Pretoria South Africa

sparse coding is an efficient way of coding information. In a sparse code most of the code elements are zero;very few are active. sparse codes are intended to correspond to the spike trains with which biological neurons communicate. In this article, we show how sparse codes can be used to do continuous speech recognition. We use the TIDIGITS dataset to illustrate the process. First a waveform is transformed into a spectrogram, and a sparse code for the spectrogram is found by means of a linear generative model. The spike train is classified by making use of a spike train model and dynamic programming. It is computationally expensive to find a sparse code. We use an iterative subset selection algorithm with quadratic programming for this process. This algorithm finds a sparse code in reasonable time if the input is limited to a fairly coarse spectral resolution. At this resolution, our system achieves a word error rate of 19%, whereas a system based on Hidden Markov Models achieves a word error rate of 15% at the same resolution. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： sparse coding Spike train Speech recognition Linear generative model

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