The Experts below are selected from a list of 4374 Experts worldwide ranked by ideXlab platform
Fengqing Qin - One of the best experts on this subject based on the ideXlab platform.
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Blind Single-Image Super Resolution Reconstruction with Gaussian Blur and Pepper & Salt Noise
Journal of Computers, 2014Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:To improve the spatial resolution of low resolution image with Gaussian Blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian Blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering method. Then, the Gaussian Blur of the de-noised image is estimated through error-parameter analysis method. Finally, super resolution reconstruction is carried out through iterative back projection algorithm. Experimental results show that the Gaussian Blur is estimated with high accuracy, and the Pepper & Salt noise are removed effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved. In addition, the importance of Gaussian Blur in single-image super resolution reconstruction is justified in an experimental way.
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blind single image super resolution reconstruction with Gaussian Blur and pepper salt noise
Journal of Computers, 2014Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:To improve the spatial resolution of low resolution image with Gaussian Blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian Blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering method. Then, the Gaussian Blur of the de-noised image is estimated through error-parameter analysis method. Finally, super resolution reconstruction is carried out through iterative back projection algorithm. Experimental results show that the Gaussian Blur is estimated with high accuracy, and the Pepper & Salt noise are removed effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved. In addition, the importance of Gaussian Blur in single-image super resolution reconstruction is justified in an experimental way.
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Blind multi-image super resolution reconstruction with Gaussian Blur and Gaussian noise
2014Co-Authors: Qin Fengqing, Fengqing QinAbstract:A framework of blind multi-image super resolution reconstruction method is proposed to improve the resolution of low resolution images with Gaussian Blur and noise. In the low resolution imaging model, the shift motion, Gaussian Blur, down-sampling, as well as Gaussian noise are all considered. Firstly, the Gaussian noise in the low resolution image is reduced through Wiener filtering method. Secondly, the Gaussian Blur of the de-noised image is estimated through error-parameter analysis method. Thirdly, the motion parameters are estimated. Finally, super resolution reconstruction is performed through iterative back projection algorithm. Experimental results show that the Gaussian Blur and motion parameters are estimated with high precision, and that the Gaussian noise is restrained effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image are enhanced. The importance of Gaussian Blur estimation and effect of Gaussian de-noising in multi-image super resolution reconstruction are tested in an experimental way.
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Blind Single-Image Super Resolution Reconstruction with Gaussian Blur
Lecture Notes in Electrical Engineering, 2013Co-Authors: Fengqing QinAbstract:To enhance the resolution of image, a framework of a blind single-image super resolution reconstruction method with Gaussian Blur is proposed. In the low resolution imaging model, the processes of Gaussian Blur, down-sampling and noise are considered. Through an error-parameter analyzing method, Gaussian point spread function is estimated automatically. Super resolution image is reconstructed through iterative back projection algorithm. Experiment is performed on a simulated low resolution image. The results show that the parameters of Gaussian point spread function are accurately estimated. The influence of Gaussian Blur estimation on blind single-image super resolution reconstruction is also justified in an experimental way. The more accurate the Gaussian Blur is estimated, the better quality of the SR image will be achieved.
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Gaussian Noised Single-Image Super Resolution Reconstruction
Applied Mechanics and Materials, 2013Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian Blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is reduced effectively and the peak signal to noise ratio (PSNR) is increased.
Wanan Yang - One of the best experts on this subject based on the ideXlab platform.
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Blind Single-Image Super Resolution Reconstruction with Gaussian Blur and Pepper & Salt Noise
Journal of Computers, 2014Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:To improve the spatial resolution of low resolution image with Gaussian Blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian Blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering method. Then, the Gaussian Blur of the de-noised image is estimated through error-parameter analysis method. Finally, super resolution reconstruction is carried out through iterative back projection algorithm. Experimental results show that the Gaussian Blur is estimated with high accuracy, and the Pepper & Salt noise are removed effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved. In addition, the importance of Gaussian Blur in single-image super resolution reconstruction is justified in an experimental way.
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blind single image super resolution reconstruction with Gaussian Blur and pepper salt noise
Journal of Computers, 2014Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:To improve the spatial resolution of low resolution image with Gaussian Blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian Blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering method. Then, the Gaussian Blur of the de-noised image is estimated through error-parameter analysis method. Finally, super resolution reconstruction is carried out through iterative back projection algorithm. Experimental results show that the Gaussian Blur is estimated with high accuracy, and the Pepper & Salt noise are removed effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved. In addition, the importance of Gaussian Blur in single-image super resolution reconstruction is justified in an experimental way.
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Gaussian Noised Single-Image Super Resolution Reconstruction
Applied Mechanics and Materials, 2013Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian Blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is reduced effectively and the peak signal to noise ratio (PSNR) is increased.
Chang-mo Yang - One of the best experts on this subject based on the ideXlab platform.
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Simulation of Blur in Transmitted Image through Transparent Plastic for Transparent OLEDs
IEEE\ OSA Journal of Display Technology, 2016Co-Authors: Hyeok-jun Kwon, Chang-mo YangAbstract:Transparent displays have received attention as next-generation displays. When attached to transparent displays, transparent plastics can control transmission of incident lights. They can extend the use of transparent displays to various applications. The transmitted image of the background through the transparent plastic appears Blurred due to the light diffusion. Major factors affecting this Blurriness include the haze and background distance. In this paper, the degree of Blurriness in the transmitted image is modelled by the Gaussian Blur kernel. With a small set of measurements from the black-and-white patch, the standard deviations of the Gaussian Blur kernel and luminance transition curves can be determined for all of the combinations of the haze and background distance. Experimental results indicate that the proposed method accurately estimates the degree of Blurriness.
Lihong Zhu - One of the best experts on this subject based on the ideXlab platform.
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Blind Single-Image Super Resolution Reconstruction with Gaussian Blur and Pepper & Salt Noise
Journal of Computers, 2014Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:To improve the spatial resolution of low resolution image with Gaussian Blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian Blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering method. Then, the Gaussian Blur of the de-noised image is estimated through error-parameter analysis method. Finally, super resolution reconstruction is carried out through iterative back projection algorithm. Experimental results show that the Gaussian Blur is estimated with high accuracy, and the Pepper & Salt noise are removed effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved. In addition, the importance of Gaussian Blur in single-image super resolution reconstruction is justified in an experimental way.
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blind single image super resolution reconstruction with Gaussian Blur and pepper salt noise
Journal of Computers, 2014Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:To improve the spatial resolution of low resolution image with Gaussian Blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian Blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering method. Then, the Gaussian Blur of the de-noised image is estimated through error-parameter analysis method. Finally, super resolution reconstruction is carried out through iterative back projection algorithm. Experimental results show that the Gaussian Blur is estimated with high accuracy, and the Pepper & Salt noise are removed effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved. In addition, the importance of Gaussian Blur in single-image super resolution reconstruction is justified in an experimental way.
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Gaussian Noised Single-Image Super Resolution Reconstruction
Applied Mechanics and Materials, 2013Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian Blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is reduced effectively and the peak signal to noise ratio (PSNR) is increased.
Lilan Cao - One of the best experts on this subject based on the ideXlab platform.
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Blind Single-Image Super Resolution Reconstruction with Gaussian Blur and Pepper & Salt Noise
Journal of Computers, 2014Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:To improve the spatial resolution of low resolution image with Gaussian Blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian Blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering method. Then, the Gaussian Blur of the de-noised image is estimated through error-parameter analysis method. Finally, super resolution reconstruction is carried out through iterative back projection algorithm. Experimental results show that the Gaussian Blur is estimated with high accuracy, and the Pepper & Salt noise are removed effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved. In addition, the importance of Gaussian Blur in single-image super resolution reconstruction is justified in an experimental way.
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blind single image super resolution reconstruction with Gaussian Blur and pepper salt noise
Journal of Computers, 2014Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:To improve the spatial resolution of low resolution image with Gaussian Blur and Pepper & salt noise, a blind single-image super resolution reconstruction method is proposed. In the low resolution imaging model, the Gaussian Blur, down-sampling, as well as Pepper & Salt noise are all considered. Firstly, the Pepper & Salt noise in the low resolution image is reduced through median filtering method. Then, the Gaussian Blur of the de-noised image is estimated through error-parameter analysis method. Finally, super resolution reconstruction is carried out through iterative back projection algorithm. Experimental results show that the Gaussian Blur is estimated with high accuracy, and the Pepper & Salt noise are removed effectively. The visual effect and peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved. In addition, the importance of Gaussian Blur in single-image super resolution reconstruction is justified in an experimental way.
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Gaussian Noised Single-Image Super Resolution Reconstruction
Applied Mechanics and Materials, 2013Co-Authors: Fengqing Qin, Lihong Zhu, Lilan Cao, Wanan YangAbstract:A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian Blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is reduced effectively and the peak signal to noise ratio (PSNR) is increased.