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Image deblurring with blur kernel estimation in RGB channels
2016 IEEE International Conference on Digital Signal Processing (DSP), 2016Image deblurring aims to recover the clear image from the damaged image. The most existing blind image de-blurring approaches only consider estimating the blur kernel in the gray domain. In fact, for the color image produced by the digital camera, the blur effects for each color channel are usually different.
Xianqiu Xu +3 more
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Robust Estimation of Motion Blur Kernel Using a Piecewise-Linear Model
IEEE Transactions on Image Processing, 2014Blur kernel estimation is a crucial step in the deblurring process for images. Estimation of the kernel, especially in the presence of noise, is easily perturbed, and the quality of the resulting deblurred images is hence degraded. Since every motion blur in a single exposure image can be represented by 2D parametric curves, we adopt a piecewise-linear
Gyeonghwan Kim
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Camera intrinsic blur kernel estimation: A reliable framework
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015This paper presents a reliable non-blind method to measure intrinsic lens blur. We first introduce an accurate camera-scene alignment framework that avoids erroneous homography estimation and camera tone curve estimation. This alignment is used to generate a sharp correspondence of a target pattern captured by the camera.
Ali Mosleh 0002 +4 more
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Image Super-Resolution Reconstruction by Blur Kernel Estimation
2023 3rd International Conference on Communication Technology and Information Technology (ICCTIT), 2023Xiaotong Liu +3 more
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Motion blur kernel estimation using noisy inertial data
2014 IEEE International Conference on Image Processing (ICIP), 2014In the case of motion blur due to unknown motion, most of the existing image deblurring algorithms rely on good initial estimate of the kernel or latent image obtained through blind deconvolution and only consider 3-dimensional camera motions. To overcome these problems, Joshi [1] presented a novel blur kernel estimation and image deblurring approach ...
Ruiwen Zhen, Robert L. Stevenson
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LBKENet:Lightweight Blur Kernel Estimation Network for Blind Image Super-Resolution
2023Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version. Most of the existing blind SR techniques use a degradation estimator network to explicitly estimate the blur kernel to guide the SR network with the supervision of ground truth (GT ...
Khan A. H. +4 more
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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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