Results 231 to 240 of about 1,764,639 (264)

Blurred Image Restoration Using Fast Blur-Kernel Estimation

2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2014
Motion blur is usually generated when people captured a picture in the daily life. This kind of blur is often non-liner motion and may cause the blurred contents seriously in this image. Hence, how to remove the blurred image into a clear image becomes a very important scheme.
Hui-Yu Huang, Wei-Chang Tsai
openaire   +1 more source

Blur kernel estimation using the radon transform

CVPR 2011, 2011
Camera shake is a common source of degradation in photographs. Restoring blurred pictures is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely underconstrained. In this work, we estimate camera shake by analyzing edges in the image, effectively constructing the Radon transform of the kernel ...
Taeg Sang Cho, Sylvain Paris, Berthold K. P. Horn, William T. Freeman   +3 more
openaire   +1 more source

Improved blur kernel estimation with blurred and noisy image pairs

2010 International Conference on Computer and Communication Technologies in Agriculture Engineering, 2010
In this paper, we propose a TV-L1 denoising model-based kernel estimation in image deblurring which uses both blurred and noisy images. More details and edges are recovered in the denoised image which is used to replace the true image and do the deconvolution.
null Qian Wan, null Yuan-Biao Zhang, null Chuan He, null Jia-Di Wan   +3 more
openaire   +1 more source

Image deblurring with blur kernel estimation in RGB channels

2016 IEEE International Conference on Digital Signal Processing (DSP), 2016
Image 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, Hongqing Liu, Yong Li 0023, Yi Zhou 0014   +3 more
openaire   +1 more source

Camera intrinsic blur kernel estimation: A reliable framework

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
This 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, Paul Green 0002, Emmanuel Onzon, Isabelle Bégin, J. M. Pierre Langlois   +4 more
openaire   +2 more sources

Motion blur kernel estimation using noisy inertial data

2014 IEEE International Conference on Image Processing (ICIP), 2014
In 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
openaire   +2 more sources

LBKENet:Lightweight Blur Kernel Estimation Network for Blind Image Super-Resolution

2023
Blind 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., Umer R. M., Dunnhofer M., Micheloni C., Martinel N.   +4 more
openaire   +3 more sources

Blur kernel estimation via salient edges and nonlocal regularization

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015
Blind 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, 2014
Restoring 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, 2011
This 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, James R. Waterman, K. Peter Judd   +2 more
openaire   +1 more source