The nonlocal self-similarity of images means that groups of similar patches have low-dimensional property. The property has been previously used for image denoising, with particularly notable success via sparsecoding...
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The nonlocal self-similarity of images means that groups of similar patches have low-dimensional property. The property has been previously used for image denoising, with particularly notable success via sparsecoding. However, only a few studies have focused on the varying statistics of noise in different similar patches during the iterative denoising process. This has motivated us to introduce an improved weighted sparse coding for gray-level image denoising in this paper. On the basis of traditional sparsecoding, we introduce a weight matrix to account for the noise variation characteristics of different similar patches, while introduce another weight matrix to make full use of the sparsity priors of natural images. The Maximum A-Posterior estimation (MAP) is used to obtain the closed-form solution of the proposed method. Experimental results demonstrate the competitiveness of the proposed method compared with that of state-of-the-art methods in both the objective and perceptual quality.
In this paper,we introduce a saliency detection method to automatically detect salient regions of different kinds of images with different complex *** method takes the advantages of efficiency and robustness of machin...
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ISBN:
(纸本)9781538629185
In this paper,we introduce a saliency detection method to automatically detect salient regions of different kinds of images with different complex *** method takes the advantages of efficiency and robustness of machine learning and sparse *** adopt concept of multiscale learning to use random forests classifier to get the training *** we construct non-saliency dictionary through the potential background information of initial map produced by using the training *** refine the saliency map and achieve better saliency performance,we utilize weighted sparse coding to compute the saliency map with the non-saliency dictionary which contains the most foreground *** experiment results indicate that our method is intuitive,effective and achieves state-of-the-art results on several benchmarks.
Joint sparse representation (JSR) has shown great potential in various image processing and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this paper, we propose a weighted JSR (W...
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Joint sparse representation (JSR) has shown great potential in various image processing and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this paper, we propose a weighted JSR (WJSR) model to simultaneously encode a set of data samples that are drawn from the same subspace but corrupted with noise and out-liers. Our model is desirable to exploit the common information shared by these data samples while reducing the influence of outliers. To solve the WJSR model, we further introduce a greedy algorithm called weighted simultaneous orthogonal matching pursuit to efficiently approximate the global optimal solution. Then, we apply the WJSR for mixed noise removal by jointly coding the grouped nonlocal similar image patches. The denoising performance is further improved by incorporating it with the global prior and the sparse errors into a unified framework. Experimental results show that our denoising method is superior to several state-of-the-art mixed noise removal methods.
Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs. SGE explores th...
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Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs. SGE explores the sparsity of the HSI data and can achieve good results. However, in most cases, locality is more important than sparsity when learning the features of the data. In this letter, we propose an extended SGE method: the weightedsparse graph based DR (WSGDR) method for HSIs. WSGDR explicitly encourages the sparsecoding to be local and pays more attention to those training pixels that are more similar to the test pixel in representing the test pixel. Furthermore, WSGDR can offer data-adaptive neighborhoods, which results in the proposed method being more robust to noise. The proposed method was tested on two widely used HSI data sets, and the results suggest that WSGDR obtains sparser representation results. Furthermore, the experimental results also confirm the superiority of the proposed WSGDR method over the other state-of-the-art DR methods.
Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challen...
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Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge for the advancement of this field. Here, we present an automated species identification method for wildlife pictures captured by remote camera traps. Our process starts with images that are cropped out of the background. We then use improved sparsecoding spatial pyramid matching (ScSPM), which extracts dense SIFT descriptor and cell-structured LBP (cLBP) as the local features, that generates global feature via weighted sparse coding and max pooling using multi-scale pyramid kernel, and classifies the images by a linear support vector machine algorithm. weighted sparse coding is used to enforce both sparsity and locality of encoding in feature space. We tested the method on a dataset with over 7,000 camera trap images of 18 species from two different field cites, and achieved an average classification accuracy of 82%. Our analysis demonstrates that the combination of SIFT and cLBP can serve as a useful technique for animal species recognition in real, complex scenarios.
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