- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Dictionaries of deep features for land-use scene classification of very high spatial resolution images
摘要： Land-use classification in very high spatial resolution images is critical in the remote sensing field. Consequently, remarkable efforts have been conducted towards developing increasingly accurate approaches for this task. In recent years, deep learning has emerged as a dominant paradigm for machine learning, and methodologies based on deep convolutional neural networks have received particular attention from the remote sensing community. These methods typically utilize transfer learning and/or data augmentation to accommodate a small number of labeled images in the publicly available datasets in this field. However, they typically require powerful computers and/or a long time for training. In this work, we propose a simple and novel method for land-use classification in very high spatial resolution images, which efficiently combines transfer learning with a sparse representation. Specifically, the proposed method performs the classification of land-use scenes using a modified version of the well-known sparse representation-based classification method. While this method directly uses the training images to form dictionaries, which are employed to classify test images, our method utilizes a pre-trained deep convolutional neural network and the Gaussian mixture model to generate more robust and compact 'dictionaries of deep features.' The effectiveness of the proposed method was evaluated on two publicly available datasets: UC Merced and Brazilian Cerrado–Savana. The experimental results suggest that our method can potentially outperform state-of-the-art techniques for land-use classification in very high spatial resolution images.
关键词： Dictionary learning,Land-use classification,Sparse representation,Feature learning,Deep learning
更新于2025-09-23 15:23:52
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[IEEE 2018 International Joint Conference on Neural Networks (IJCNN) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 International Joint Conference on Neural Networks (IJCNN) - Multi-spectral missing label prediction via restoration using deep residual dictionary learning
摘要： Dictionary learning (DL) is one of the popular sparse coding machine learning techniques. In image processing literature, every input image is represented as the sparse linear combination of basis vectors. DL has been shown to have wide applications for image restoration as well as pattern recognition problems. In DL, the input image is factorized into dictionary and sparse codes. This factorization always leaves a residual or approximation error. Very few works in the literature had focused on to leverage the information present in this residual. In this paper, we use residuals within our framework and show that the restoration performance or accurate prediction of missing label in multi-spectral images can be significantly improved over conventional DL based techniques. We initially show that the higher order frequencies are propagated through residuals. Then we show that incorporating this residual in the image restoration methodology can significantly improve the outcomes. Finally, we propose a technique to solve the problem of missing label prediction by using a restoration based deep residual dictionary learning framework.
关键词： Dictionary learning,Restoration,Sparse coding,In-painting,Residuals,Label prediction
更新于2025-09-23 15:23:52
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OPD analysis and prediction in aero-optics based on dictionary learning
摘要： When aircraft ?ying at a high speed, the density and re?ective index of atmosphere around it become uneven. Thus images or videos observed from the observation window on the aircraft are usually blur or quivering, which is called the aero-optical effect. To recover the images from poor quality, it is necessary to learn about the wavefront distortion of the light, described as optical path difference (OPD). Among the existing methods, the method of computational ?uid dynamics (CFD) simulation followed by ray tracing is very time consuming, and the method of real-time OPD measurement with OPD sensor has a certain lag for OPD with high frequency. In this paper, a reconstructible dimension reduction method based on dictionary learning is employed to map the high-dimensional OPD data into a low-dimensional space, and the OPD data are calculated when rays travel across the supersonic shear layer. All the parameters of training and test datasets remain the same except the convective Mach numbers (Mc number). According to the dimension reduction results of training sets, we ?nd that OPD is obviously periodic and its distribution characteristics have a strong correlation with Mc number. By ?tting the OPD data in the low-dimensional space, every point on the ?tting curve can be reconstructed to the original high-dimensional space, which works as prediction. Compared with the truthful data, the average similarity coef?cient of the prediction for the test datasets is up to 83%, which means that the prediction result is credible.
关键词： Prediction,Aero-optics,Couple dictionary learning,Optical path difference
更新于2025-09-23 15:23:52
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Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests
摘要： Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.
关键词： super-resolution,coupled dictionary learning,random forests,low-dose CT
更新于2025-09-23 15:22:29
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Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution
摘要： This paper presents a hyperspectral image (HSI) super-resolution method which fuses a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t-product) based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and LR-HSI is built using t-product which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, Alternating Direction Method of Multipliers (ADMM) is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods.
关键词： tensor dictionary learning,Hyperspectral image,nonlocal patch tensor,tensor sparse coding,super-resolution
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Ge virtual substrates for high efficiency III-V solar cells: applications, potential and challenges
摘要： Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, ?lm production, and medical rehabilita- tion. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high com- plexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal pat- terns and the structural sparsity embedded in motion data. We ?rst replace the regularly used entire pose model with a much ?ne-grained partlet model as feature representation to exploit the abundant local body part posture and movement similari- ties. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution informa- tion and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.
关键词： (cid:2)2,p-norm,robust dictionary learning,Microsoft Kinect,robust structured sparse coding,motion capture data,Human motion denoising
更新于2025-09-23 15:19:57
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Reconstruction of compressively sampled light field by using tensor dictionaries
摘要： How to capture the high quality light field photography was one of important issue in computational photography. In fact, light field could be captured directly for all views or compressively reconstructed for each view just through one coded image. The latter kind of method was more feasible since only one exposure was needed for all views, among which dictionary-based light field reconstruction had been shown its effectiveness. In this paper, a more effective light field reconstruction method based on tensor dictionary was created. The proposed method is efficient because the trained tensor form dictionary can make better use of the rich structure of light field. Specifically, multiple small dictionaries were trained at the same time, and then were combined to a big dictionary using Kronecker product. Experimental results demonstrate the proposed method outperforms a state-of-the-art reconstruction method with the vector-form dictionary, in terms of higher reconstruction PSNR while reducing the scale of dictionary substantially.
关键词： Dictionary learning,Compressive sensing,Tensor,Light field
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Optimization of the Light Trapping Nano Structures in CdTe Thin Film Solar Cells
摘要： Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.
关键词： Vector sparse representation,K-QSVD,dictionary learning,quaternion matrix analysis,image restoration,color image
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Preliminary Study on Super Radiation-Resistant Mechanical-Stack Triple-Junction Space Solar Cell: PHOENIX
摘要： Stained glass windows are designed to reveal their powerful artistry under diverse and time-varying lighting conditions; virtual relighting of stained glass, therefore, represents an exceptional tool for the appreciation of this age old art form. However, as opposed to most other artifacts, stained glass windows are extremely difficult if not impossible to analyze using controlled illumination because of their size and position. In this paper, we present novel methods built upon image based priors to perform virtual relighting of stained glass artwork by acquiring the actual light transport properties of a given artifact. In a preprocessing step, we build a material-dependent dictionary for light transport by studying the scattering properties of glass samples in a laboratory setup. We can now use the dictionary to recover a light transport matrix in two ways: under controlled illuminations the dictionary constitutes a sparsifying basis for a compressive sensing acquisition, while in the case of uncontrolled illuminations the dictionary is used to perform sparse regularization. The proposed basis preserves volume impurities and we show that the retrieved light transport matrix is heterogeneous, as in the case of real world objects. We present the rendering results of several stained glass artifacts, including the Rose Window of the Cathedral of Lausanne, digitized using the presented methods.
关键词： light transport,recovery,dictionary learning,Banded matrices,stained glass,sparse cultural artifacts,computational relighting
更新于2025-09-23 15:19:57
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[IEEE 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) - Aalborg, Denmark (2018.9.17-2018.9.20)] 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) - K-SVD with a Real ?<inf>0</inf> Optimization: Application to Image Denoising
摘要： This paper deals with sparse coding for dictionary learning in sparse representations. Because sparse coding involves an (cid:96)0-norm, most, if not all, existing solutions only provide an approximate solution. Instead, in this paper, a real (cid:96)0 optimization is considered for the sparse coding problem providing a global optimal solution. The proposed method reformulates the optimization problem as a Mixed-Integer Quadratic Program (MIQP), allowing then to obtain the global optimal solution by using an off-the-shelf solver. Because computing time is the main disadvantage of this approach, two techniques are proposed to improve its computational speed. One is to add suitable constraints and the other to use an appropriate initialization. The results obtained on an image denoising task demonstrate the feasibility of the MIQP approach for processing well-known benchmark images while achieving good performance compared with the most advanced methods.
关键词： image denoising,dictionary learning,Mixed-integer quadratic programming,sparse representation,K-SVD,sparse coding
更新于2025-09-23 15:19:57