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Surface-Aware Blind Image Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Blind image deblurring is a conundrum because there are infinitely many pairs of latent image and blur kernel. To get a stable and reasonable deblurred image, proper prior knowledge of the latent image and the blur kernel is urgently required. Different from the recent works on the statistical observations of the difference between the blurred image ...
Jun Liu, Ming Yan, Tieyong Zeng
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2021 6th International Conference for Convergence in Technology (I2CT), 2021
With the growth of technology and digital media, images have started playing a critical role not only in our day-to-day lives but also in fields like Biology and Astronomy to name a few. However, images prove to be useful only when they can successfully convey information to a viewer.
Vahida Attar +3 more
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With the growth of technology and digital media, images have started playing a critical role not only in our day-to-day lives but also in fields like Biology and Astronomy to name a few. However, images prove to be useful only when they can successfully convey information to a viewer.
Vahida Attar +3 more
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2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0-1 loss. A novel bound allows the calculation of very high-dimensional integrals in closed form.
Meiguang Jin, Stefan Roth, Paolo Favaro
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We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0-1 loss. A novel bound allows the calculation of very high-dimensional integrals in closed form.
Meiguang Jin, Stefan Roth, Paolo Favaro
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Blind and Semi-Blind Deblurring of Natural Images
IEEE Transactions on Image Processing, 2010A method for blind image deblurring is presented. The method only makes weak assumptions about the blurring filter and is able to undo a wide variety of blurring degradations. To overcome the ill-posedness of the blind image deblurring problem, the method includes a learning technique which initially focuses on the main edges of the image and gradually
Mariana S C, Almeida, Luís B, Almeida
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Blind Deblurring for Saturated Images
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that pixels around saturated regions are not conforming to the commonly used linear blur model.
Liang Chen +4 more
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Blind deblurring of spiral CT images
IEEE Transactions on Medical Imaging, 2001To discriminate fine anatomical features in the inner ear, it has been desirable that spiral computed tomography (CT) may perform beyond their current resolution limits with the aid of digital image processing techniques. In this paper, we develop a blind deblurring approach to enhance image resolution retrospectively without complete knowledge of the ...
Ming, Jiang +4 more
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Single image blind motion deblurring
SPIE Proceedings, 2016Recovering a latent image from its blurry version is a severely ill-posed problem. In this paper a post process method is proposed for accurately estimating motion blur kernel based on its prior knowledge. And considering the small details destroy blur kernel estimation, an image decomposition process is executed before the estimation, which can ...
Bingbing Duan, Yi Li
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Blind face images deblurring with enhancement
Multimedia Tools and Applications, 2020Face images deblurring has achieved advanced development; however, existing methods involve high computational cost problems. Furthermore, the recovered face images by current methods have the problems of over-smooth textures, ringing artifacts, and poor details. We consider the problem of face images deblurring as a semantic generation task.
Qing Qi +3 more
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