The Experts below are selected from a list of 26793 Experts worldwide ranked by ideXlab platform
Xu Lei - One of the best experts on this subject based on the ideXlab platform.
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Image Interpolation Algorithm Based on Pulse Coupled Neural Network
Computer Engineering, 2012Co-Authors: Xu LeiAbstract:To reduce the edge sawtooth and blur of Image during the digital Image Interpolation,an Image Interpolation algorithm based on Pulse Coupled Neural Network(PCNN) is introduced.The clusters and the propagation paths of pulse are obtained by using the synchronous pulse burst property of PCNN,then the different Interpolation method is used in the inner and intervals of clusters to finish Interpolation of the whole Image.Experimental results show that the visual effect of Interpolation algorithm is much better than that of the bilinear and spline algorithms,and the Peak Signal to Noise Ratio(PSNR) is increased by more than 0.2 dB.
Ching-han Chen - One of the best experts on this subject based on the ideXlab platform.
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Anisotropic Probabilistic Neural Network for Image Interpolation
Journal of Mathematical Imaging and Vision, 2013Co-Authors: Ching-han Chen, Chia-ming Kuo, Tun-kai Yao, Sheng-hsien HsiehAbstract:This study proposes a novel Image Interpolation method based on an anisotropic probabilistic neural network (APNN). The proposed method uses an anisotropic Gaussian kernel to improve Image Interpolation, which causes blurred edges. The objective of this anisotropic Gaussian kernel-based probabilistic neural network is to provide high adaptivity of smoothness/sharpness during Image/video Interpolation. This APNN Interpolation method adjusts the smoothing parameters for varied smooth/edge regions, and considers edge direction. This APNN uses a single neuron to estimate sharpness/smoothness. The proposed method achieves better sharpness enhancement at edge regions, and reveals the noise reduction at smooth region. This study also uses interpolating a slanted-edge Image to reveal blurring and blocking effects. Finally, this study compares the performance of these proposed methods with other Image Interpolation methods.
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Adaptive Image Interpolation using probabilistic neural network
Expert Systems with Applications, 2009Co-Authors: Sheng-hsien Hsieh, Ching-han ChenAbstract:This paper proposes an Image Interpolation model based on probabilistic neural network (PNN). The method adjusts automatically the smoothing parameters for varied smooth/edge Image region, and takes into consideration both smoothness (flat region) and sharpness (edge region) characteristics at the same model. A single neuron, combined with PSO training, is used for sharpness/smoothness adaptation. Finally, we report the performance of these newly proposed methods in other Image Interpolation method.
Sheng-hsien Hsieh - One of the best experts on this subject based on the ideXlab platform.
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Anisotropic Probabilistic Neural Network for Image Interpolation
Journal of Mathematical Imaging and Vision, 2013Co-Authors: Ching-han Chen, Chia-ming Kuo, Tun-kai Yao, Sheng-hsien HsiehAbstract:This study proposes a novel Image Interpolation method based on an anisotropic probabilistic neural network (APNN). The proposed method uses an anisotropic Gaussian kernel to improve Image Interpolation, which causes blurred edges. The objective of this anisotropic Gaussian kernel-based probabilistic neural network is to provide high adaptivity of smoothness/sharpness during Image/video Interpolation. This APNN Interpolation method adjusts the smoothing parameters for varied smooth/edge regions, and considers edge direction. This APNN uses a single neuron to estimate sharpness/smoothness. The proposed method achieves better sharpness enhancement at edge regions, and reveals the noise reduction at smooth region. This study also uses interpolating a slanted-edge Image to reveal blurring and blocking effects. Finally, this study compares the performance of these proposed methods with other Image Interpolation methods.
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Adaptive Image Interpolation using probabilistic neural network
Expert Systems with Applications, 2009Co-Authors: Sheng-hsien Hsieh, Ching-han ChenAbstract:This paper proposes an Image Interpolation model based on probabilistic neural network (PNN). The method adjusts automatically the smoothing parameters for varied smooth/edge Image region, and takes into consideration both smoothness (flat region) and sharpness (edge region) characteristics at the same model. A single neuron, combined with PSO training, is used for sharpness/smoothness adaptation. Finally, we report the performance of these newly proposed methods in other Image Interpolation method.
Yong Dou - One of the best experts on this subject based on the ideXlab platform.
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Variational single Image Interpolation with time-varying regularization
Signal Processing: Image Communication, 2017Co-Authors: Peng Qiao, Yunjin Chen, Yong DouAbstract:Abstract Single Image Interpolation has wide applications in digital photography and Image display. Most single Image Interpolation approaches achieve state-of-the-art performance at the expense of very high computation time. While efficient alternatives exist, they do not reach the same level of Image quality. In this paper, we propose an Image Interpolation method offering both high computational efficiency and high Interpolation quality. We exploit a newly-developed variational framework with time-varying regularization, i.e., the parameters of the regularization are allowed to change with time, making it different to conventional variational problems with time-independent regularization parameters. These time-varying parameters are learned from training samples. We train the model parameters for the problem of single Image Interpolation. Experiments show that the trained models lead to promising quality of the interpolated Images in terms of quantitative measurements (e.g., PSNR and SSIM), compared with the state-of-the-art approaches. Meanwhile, high computational efficiency is obtained.
Michael Elad - One of the best experts on this subject based on the ideXlab platform.
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single Image Interpolation via adaptive nonlocal sparsity based modeling
IEEE Transactions on Image Processing, 2014Co-Authors: Yaniv Romano, Matan Protter, Michael EladAbstract:Single Image Interpolation is a central and extensively studied problem in Image processing. A common approach toward the treatment of this problem in recent years is to divide the given Image into overlapping patches and process each of them based on a model for natural Image patches. Adaptive sparse representation modeling is one such promising Image prior, which has been shown to be powerful in filling-in missing pixels in an Image. Another force that such algorithms may use is the self-similarity that exists within natural Images. Processing groups of related patches together exploits their correspondence, leading often times to improved results. In this paper, we propose a novel Image Interpolation method, which combines these two forces—nonlocal self-similarities and sparse representation modeling. The proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve state-of-the-art results.