The Experts below are selected from a list of 5241 Experts worldwide ranked by ideXlab platform
W. Li - One of the best experts on this subject based on the ideXlab platform.
-
Image visual quality restoration by cancellation of the unmasked noise
Proceedings of ICASSP '94. IEEE International Conference on Acoustics Speech and Signal Processing, 1994Co-Authors: B. Macq, M. Mattavelli, O. Van Calster, E. Van Der Plancke, S. Comes, W. LiAbstract:The aim of Image restoration is to find an estimate of the Ideal Image using a priori information about blur and/or noise and/or the Ideal Image. Classical criterion are minimum least square, minimum mean square error or maximum a posteriori probability. The choice of the criterion used to measure the estimation quality is crucial for the design of the restoration algorithm. The authors propose a new criterion based on a visual model: it is based on perceptual masking. Thereafter, they propose a new restoration algorithm dealing only with additive noise. The perceptual components of the Image to restore which are corrupted by an additive noise above a visibility threshold are simply set to zero. Some results obtained for the post-processing of JPEG Images are presented.
-
ICASSP (5) - Image visual quality restoration by cancellation of the unmasked noise
Proceedings of ICASSP '94. IEEE International Conference on Acoustics Speech and Signal Processing, 1994Co-Authors: B. Macq, M. Mattavelli, O. Van Calster, E. Van Der Plancke, S. Comes, W. LiAbstract:The aim of Image restoration is to find an estimate of the Ideal Image using a priori information about blur and/or noise and/or the Ideal Image. Classical criterion are minimum least square, minimum mean square error or maximum a posteriori probability. The choice of the criterion used to measure the estimation quality is crucial for the design of the restoration algorithm. The authors propose a new criterion based on a visual model: it is based on perceptual masking. Thereafter, they propose a new restoration algorithm dealing only with additive noise. The perceptual components of the Image to restore which are corrupted by an additive noise above a visibility threshold are simply set to zero. Some results obtained for the post-processing of JPEG Images are presented. >
Tomio Goto - One of the best experts on this subject based on the ideXlab platform.
-
ICCE - PSF estimation using total variation regularization and shock filter for blind deconvolution
2017 IEEE International Conference on Consumer Electronics (ICCE), 2020Co-Authors: Hiroki Senshiki, Tomio Goto, Satoshi Motohashi, Haifeng Chen, Reo AokiAbstract:Blind deconvolution, which restores a clear Ideal Image from a single blur Image, is the ill-posed problem of finding two unknowns: the point spread function (PSF) and the Ideal Image. In this paper, we propose a novel PSF estimation using total variation regularization and the shock filter. The experimental results show that our method is the best of several methods with respect to deblurring performance. Furthermore, our proposed method creates high quality Images for consumer cameras and smartphones with a camera.
-
Performance Improvement for Blurred Images Utilizing Learning-Based Image Restoration Method
2020 2nd International Conference on Computer Communication and the Internet (ICCCI), 2020Co-Authors: Masaki Hongo, Tomio GotoAbstract:Blind Image restoration, which restores a clear Image from a single blurry Image, is a difficult process of estimating two unknowns: a point-spread function (PSF) and an Ideal Image. In this paper, we use a learning-type restoration method that learns a sharp Image without blur and an Image that contains blur, and supposes the restoration of blurred Images that occur especially when shooting fast-moving objects such as cars. We study the improvement of the Image quality of the restored Image by examining the training Image used for the Image.
-
GCCE - Performance Improvement of Blind Image Restoration Using Ringing Removal Processing
2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), 2019Co-Authors: Masahiro Goto, Tomio GotoAbstract:Blur is a representative feature of Image degradation. The restoration of a degraded Image can be successfully achieved using a method in which point spread function estimation and Ideal Image estimation processing are alternately repeated. In this paper, we introduce a ringing removal method using L 0 regularization and propose a highperformance restoration method that can estimate sharper Images. It is known that the prior probability of the gradient is effective in suppressing deterioration such as ringing. Thus, Ideal Image estimation is performed only using gradient information. In the proposed method, a bilateral filter is used to compare the estimated Ideal Image obtained in previous studies with that obtained using gradient information. By subtracting the difference map from the final deconvolution result, a clear Image without ringing could be obtained.
-
Performance Improvement of Blind Image Restoration Using Ringing Removal Processing
2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), 2019Co-Authors: Masahiro Goto, Tomio GotoAbstract:Blur is a representative feature of Image degradation. The restoration of a degraded Image can be successfully achieved using a method in which point spread function estimation and Ideal Image estimation processing are alternately repeated. In this paper, we introduce a ringing removal method using L0 regularization and propose a highperformance restoration method that can estimate sharper Images. It is known that the prior probability of the gradient is effective in suppressing deterioration such as ringing. Thus, Ideal Image estimation is performed only using gradient information. In the proposed method, a bilateral filter is used to compare the estimated Ideal Image obtained in previous studies with that obtained using gradient information. By subtracting the difference map from the final deconvolution result, a clear Image without ringing could be obtained.
-
A study on blind Image restoration of blurred Images using R-map
2018 International Workshop on Advanced Image Technology (IWAIT), 2018Co-Authors: Satoshi Motohashi, Tomio Goto, Reo Aoki, Takahiro Nagata, Haifeng ChenAbstract:Image restoration which restores a clear Image from a single blur Image is a difficult problem of estimating two unknowns: a point spread function (PSF) and its Ideal Image. In this paper, we propose a novel blind deconvolution method to alternately estimate PSF and the latent Image. And we incorporate the gradient reliability map (R-map) that enables edge selection appropriate for PSF estimation processing. This method improves restoration performance by excluding noise that adversely affects the estimation, and the experimental results show that robustness is improved in our proposed method.
B. Macq - One of the best experts on this subject based on the ideXlab platform.
-
Image visual quality restoration by cancellation of the unmasked noise
Proceedings of ICASSP '94. IEEE International Conference on Acoustics Speech and Signal Processing, 1994Co-Authors: B. Macq, M. Mattavelli, O. Van Calster, E. Van Der Plancke, S. Comes, W. LiAbstract:The aim of Image restoration is to find an estimate of the Ideal Image using a priori information about blur and/or noise and/or the Ideal Image. Classical criterion are minimum least square, minimum mean square error or maximum a posteriori probability. The choice of the criterion used to measure the estimation quality is crucial for the design of the restoration algorithm. The authors propose a new criterion based on a visual model: it is based on perceptual masking. Thereafter, they propose a new restoration algorithm dealing only with additive noise. The perceptual components of the Image to restore which are corrupted by an additive noise above a visibility threshold are simply set to zero. Some results obtained for the post-processing of JPEG Images are presented.
-
ICASSP (5) - Image visual quality restoration by cancellation of the unmasked noise
Proceedings of ICASSP '94. IEEE International Conference on Acoustics Speech and Signal Processing, 1994Co-Authors: B. Macq, M. Mattavelli, O. Van Calster, E. Van Der Plancke, S. Comes, W. LiAbstract:The aim of Image restoration is to find an estimate of the Ideal Image using a priori information about blur and/or noise and/or the Ideal Image. Classical criterion are minimum least square, minimum mean square error or maximum a posteriori probability. The choice of the criterion used to measure the estimation quality is crucial for the design of the restoration algorithm. The authors propose a new criterion based on a visual model: it is based on perceptual masking. Thereafter, they propose a new restoration algorithm dealing only with additive noise. The perceptual components of the Image to restore which are corrupted by an additive noise above a visibility threshold are simply set to zero. Some results obtained for the post-processing of JPEG Images are presented. >
Reo Aoki - One of the best experts on this subject based on the ideXlab platform.
-
ICCE - PSF estimation using total variation regularization and shock filter for blind deconvolution
2017 IEEE International Conference on Consumer Electronics (ICCE), 2020Co-Authors: Hiroki Senshiki, Tomio Goto, Satoshi Motohashi, Haifeng Chen, Reo AokiAbstract:Blind deconvolution, which restores a clear Ideal Image from a single blur Image, is the ill-posed problem of finding two unknowns: the point spread function (PSF) and the Ideal Image. In this paper, we propose a novel PSF estimation using total variation regularization and the shock filter. The experimental results show that our method is the best of several methods with respect to deblurring performance. Furthermore, our proposed method creates high quality Images for consumer cameras and smartphones with a camera.
-
A study on blind Image restoration of blurred Images using R-map
2018 International Workshop on Advanced Image Technology (IWAIT), 2018Co-Authors: Satoshi Motohashi, Tomio Goto, Reo Aoki, Takahiro Nagata, Haifeng ChenAbstract:Image restoration which restores a clear Image from a single blur Image is a difficult problem of estimating two unknowns: a point spread function (PSF) and its Ideal Image. In this paper, we propose a novel blind deconvolution method to alternately estimate PSF and the latent Image. And we incorporate the gradient reliability map (R-map) that enables edge selection appropriate for PSF estimation processing. This method improves restoration performance by excluding noise that adversely affects the estimation, and the experimental results show that robustness is improved in our proposed method.
-
PSF estimation using total variation regularization and shock filter for blind deconvolution
2017 IEEE International Conference on Consumer Electronics (ICCE), 2017Co-Authors: Hiroki Senshiki, Tomio Goto, Satoshi Motohashi, Haifeng Chen, Reo AokiAbstract:Blind deconvolution, which restores a clear Ideal Image from a single blur Image, is the ill-posed problem of finding two unknowns: the point spread function (PSF) and the Ideal Image. In this paper, we propose a novel PSF estimation using total variation regularization and the shock filter. The experimental results show that our method is the best of several methods with respect to deblurring performance. Furthermore, our proposed method creates high quality Images for consumer cameras and smartphones with a camera.
Rob Elton - One of the best experts on this subject based on the ideXlab platform.
-
healthy or druggy self Image Ideal Image and smoking behaviour among young people
Social Science & Medicine, 1997Co-Authors: Amanda Amos, David Gray, Candace Currie, Rob EltonAbstract:Recent research indicates that there is an important, though complex, relationship between the social Image of smoking and young people's self- and aspirational Images. This study explored how young people see themselves (self-Image), how they would like to be (Ideal Image), and whether these differ according to age, gender and smoking status. Focus groups were used to elicit attributes which young people use to describe smoking and non-smoking Images taken from fashion pages in youth magazines. These attributes were incorporated into a self-completion questionnaire which was administered to 897 young people from three age groups (12-13 years, 15-16 years and 18-19 years). The respondents rated their self- and Ideal Images on each of these attributes. Overall, there were few differences between the rank order of attributes by age, sex or smoking status. However, there were differences in the trait scores, with males and smokers tending to rate themselves more positively. The two traits which most clearly differentiated smokers and non-smokers were druggy/takes drugs (self- and Ideal Image) and healthy (self-Image). It appears that smokers in general, and male smokers in particular, embraced certain dimensions of self- and aspirational Image of which druggy, tough and tarty are signifiers. In contrast, the differences between female smokers and non-smokers were less consistent and differed with age. The implications for health promotion are discussed.
