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Recently, with the development of visual communication and image processing, there is a high demand for high-resolution images such as video surveillance, medical imaging, and so on. Therefore, the super-resolution technology that produces a high-resolution image from a set of shifted, blurred, and decimated versions is actively researched. However, most previously published techniques perform well...
Subspace learning techniques are widespread in pattern recognition research. They include PCA, ICA, LPP, etc. These techniques are generally linear and unsupervised. The problem of image indexing is very complicated and the processed images are usually lie on non-linear image subspaces. In this paper, we propose a supervised nonlinear neighborhood embedding algorithm which learns an adaptive nonlinear...
This paper presents an auto-annotation system with simple pre-processed segmentation for digital color image. Recently, annotation techniques become one popular method for image retrieval system in image database management, image recognition system and so on. In the paper, we propose a two-step approach for image annotation. Firstly, the color image is needed to be segmented into two parts: the main...
At present there are many methods that could deal well with frontal view face recognition when there is sufficient number of representative training samples. There into, subspace learning method such as principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA) are a very hot research topic in this field. However, in some face recognition system, the...
The reduction of noise in medical images is an important issue. In this paper, we propose a new ICA-based filter for reduction of noise in reconstruction domain. In the proposed filter, the reconstructed 3D PET images(X-Y plane-slice domain or X-Z plane) are firstly transformed to ICA domain, and then, the components of noise information are removed by a soft thresholding (shrinkage). In this study,...
This paper proposes a new method based on independent component analysis (ICA) for Poisson noise reduction. In the proposed method, the image is first transformed to ICA domain and then the noise components are removed by a soft thresholding (Shrinkage). The proposed method, which is used as a preprocessing of the reconstruction, has been successfully applied to penumbral imaging. Both simulation...
Projection data in positron emission tomography (PET) are acquired as a number of photon counts from different observation angles. Positron decay is a random phenomenon that causes undesirably high variations in measured sinogram appearing as quantum noise. The ruduction of quantum noise or Poisson noise in medical images is an important issue. In this paper, we propose a new ICA-based filter for...
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