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Blur kernel estimation via salient edges and nonlocal regularization
2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015Blind image deblurring is a severely ill-posed inverse problem. To obtain a high quality latent image from a single blurred one, effective regularizations are required. In this paper, we propose a nonlocal regularization to improve blur kernel estimation.
Suil Son, Suk I. Yoo
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Blur kernel estimate in single noisy image deblurring
SPIE Proceedings, 2014Restoring blurred images is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely under constrained. Recently many single image blind deconvolution methods have been proposed, but these state-of-the-art single image deblurring techniques are still sensitive to image noise, and can degrade their ...
Shijie Sun, Huaici Zhao, Bo Li
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A new blur kernel estimator and comparisons to state-of-the-art
SPIE Proceedings, 2011This paper presents a simple, fast, and robust method to estimate the blur kernel model, support size, and its parameters directly from a blurry image. The edge profile method eliminates the need for searching the parameter space. In addition, this edge profile method is highly local and can provide a measure of asymmetry and spatial variation ...
Leslie N. Smith +2 more
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Blur Kernel Optimization: A New Approach to Patch Selection with Adaptive Kernel Estimation
Applied Mechanics and Materials, 2013Recently, many effective approaches appeared in the field of blind image deconvolution to reduce the computational cost. Using multiple smaller regions instead of whole image not only make the restoration efficient but also improves the results by discarding the ineffectual regions. It is observed that a study is needed to compare different methods for
Saqib Yousaf, Shi Yin Qin
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5th International Conference on Computer Sciences and Convergence Information Technology, 2010
Optical flow methods, such as Lucas-Kanade and Horn-Schunck algorithms, are popular in motion estimation. However, they fall short on accuracy when they are applied to blurred videos. Some people utilize hybrid camera system to get a low resolution image to suppress the blurring effect so that more accurate optical flow for blurred high resolution ...
Luo, T +5 more
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Optical flow methods, such as Lucas-Kanade and Horn-Schunck algorithms, are popular in motion estimation. However, they fall short on accuracy when they are applied to blurred videos. Some people utilize hybrid camera system to get a low resolution image to suppress the blurring effect so that more accurate optical flow for blurred high resolution ...
Luo, T +5 more
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Acoustic blur kernel with sliding window for blind estimation of reverberation time
2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2015Reverberation time, or T 60 , is a key parameter used for characterizing acoustic spaces. Blind T 60 estimation is useful for many applications including speech intelligibility estimation, acoustic scene analysis and dereverberation. In our previous work, a single-channel blind T 60 estimator was proposed employing spectral analysis in the modulation
Felicia Lim +3 more
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Spatial-scale-regularized blur kernel estimation for blind image deblurring
Signal Processing: Image Communication, 2018Abstract Blind image deblurring is a long-standing and challenging inverse problem in image processing. In this paper, we propose a new spatial-scale-regularized approach to estimate a blur kernel (BK) from a single motion blurred image by regularizing the spatial scale sizes of image edges.
Shu Tang +5 more
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Hybrid Regularized Blur Kernel Estimation for Single-Image Blind Deconvolution
2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015Single-image blind deconvolution is a challenging illposed inverse problem which requires regularization techniques to stabilize the restoration process. Its purpose is to recover an underlying blur kernel and a latent image from only one blurred image.
Ryan Wen Liu +3 more
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Spatial-scale-based blur kernel estimation for blind motion deblurring
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 2017Maximum a posteriori (MAP)-based single-image blind motion deblurring methods are extensively studied in the past years, and have achieved great progress. However, because of imperfect salient edges selection, most state-of-the-art methods still cannot estimate the blur kernel (BK) accurately, especially in large motion blur cases.
Shu Tang +4 more
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Blur Kernel Estimation Model with Combined Constraints for Blind Image Deblurring
2018 Digital Image Computing: Techniques and Applications (DICTA), 2018This paper proposes a blur kernel estimation model based on combined constraints involving both image and blur kernel constraints for blind image deblurring. We adopt L0 regularization term for constraining image gradient and dark channel of image gradient to protect image strong edges and suppress noise in image, and use L2 regularization term as ...
Ying Liao +3 more
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