Low Cost Robust Blur Estimator

Hu, Hao; Hann, G. De

doi:10.1109/icip.2006.312411

Cited by 70 publications

(62 citation statements)

References 7 publications

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“…Other terms are spike noise, random noise or independent noise. Black and white dots appear in the image explained in [5] as a result of this noise and hence salt and pepper noise. This noise arises in the image because of sharp and sudden changes of the image signal.…”

Section: Restoration Techniques 31 Noise Modelsmentioning

confidence: 99%

“…The idea of image restoration is to minimize the noise [5,2] and blurring image [4,2] from a degraded image by various atmospheric defects. It introduces an effective method for image restoration is de-convolution based on a coefficient of image formulated in the wavelet domain wavelet based denoising of an image can be done since Donoho in [9].…”

Section: Introductionmentioning

confidence: 99%

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Computation Pre-processing Techniques for Image Restoration

Makandar¹,

Patrot²

2015

IJCA

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Image restoration is to enhance the image quality which is blurred and noised from various defects which damage the quality of an image. The most degradation is done in motion blur and noise defects as shown in the results. This introduces and implements the computing methods used in the image processing world to restore images as well as improve the quality by threshold. In order to know the detailed information carried in the digital image for better visualization. The aim is to provide information of image degradation and restoration process by various filters such as wiener filter, blind convolution and wavelet techniques are used in experiments in this paper will be presented as followed by MATLAB simulation results. Weiner filter gives maximum PSNR value and minimum MSE value in dB comparable to other techniques for image restoration.

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Section: Restoration Techniques 31 Noise Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computation Pre-processing Techniques for Image Restoration

Makandar¹,

Patrot²

2015

IJCA

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“…Most of the existing works perform blind image deconvolution based on a single image [15]. A PSF is inferred through total variation regularization [16], the variation of Gaussian scale in the edges [17][18] [19], or variation in the wavelet domain [20] [21]. Recently, M. Nishiyama [6] has revealed that deblurring from a single image is an ill-posed problem and these deblurring methods are insufficient for accurate face recognition.…”

Section: Introductionmentioning

confidence: 99%

Blur-Robust Face Recognition via Transformation Learning

Zhang

et al. 2015

Lecture Notes in Computer Science

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Abstract. This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred images to a same feature subspace by the method of multidimensional scaling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the manifold structure of the original shape images and, at the same time, enhances the class separability, resulting in a wide applications to various descriptors. Furthermore, we combine our method with subspace-based point spread function (PSF) estimation method to handle cases of unknown blur degree, by applying the feature transformation corresponding to the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance against the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.

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“…The proposed method adopts a simple non-iterative blur estimator, as proposed in our earlier work [4]. The blur estimator uses a Gaussian isotropic PSF model and the difference between digitally re-blurred versions of an image is used to estimate the blur radius without edge detection.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Image Restoration Based on Local Robust Blur Estimation

Haan

2007

Advanced Concepts for Intelligent Vision Systems

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Abstract. This paper presents a novel non-iterative method to restore the out-of-focus part of an image. The proposed method first applies a robust local blur estimation to obtain a blur map of the image. The estimation uses the maximum of difference ratio between the original image and its two digitally re-blurred versions to estimate the local blur radius. Then adaptive least mean square filters based on the local blur radius and the image structure are applied to restore the image and to eliminate the sensor noise. Experimental results have shown that despite its low complexity the proposed method has a good performance at reducing spatially varying blur.

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Low Cost Robust Blur Estimator

Cited by 70 publications

References 7 publications

Computation Pre-processing Techniques for Image Restoration

Computation Pre-processing Techniques for Image Restoration

Blur-Robust Face Recognition via Transformation Learning

Adaptive Image Restoration Based on Local Robust Blur Estimation

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