Adaptive Image Deblurring with Ringing Control

Krylov, Andrey S.; Nasonov, Andrey

doi:10.1109/icig.2009.136

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…The result for combination of unsharp mask and regularized total variation (TV) deconvolution in low frequency domain [8] for the problem of image deblurring is shown in Fig. 6.…”

Section: Resultsmentioning

confidence: 99%

Edge quality metrics for image enhancement

Nasonov

Krylov

2012

Pattern Recognit. Image Anal.

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The paper presents a new adaptive full reference method for quality measurement of image enhancement algorithms. The method is based on the analysis of basic edges-sharp edges which are distant from another edges. The proposed basic edges metrics calculates error values in two areas related to typical artifacts of image enhancement algorithms: basic edges area and basic edges neighborhood. The metrics are illustrated with an application to image resampling and image deblurring but it is also applicable for image deringing and image denoising.

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“…The result for combination of unsharp mask and regularized total variation (TV) deconvolution in low frequency domain [8] for the problem of image deblurring is shown in Fig. 6.…”

Section: Resultsmentioning

confidence: 99%

Edge quality metrics for image enhancement

Nasonov

Krylov

2012

Pattern Recognit. Image Anal.

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“…To be able to precisely predict the image quality perceived by a standard observer would be highly beneficial for a variety of applications like TV broadcasting or video conferencing quality control [21]- [28], still image compression evaluation for medical and end user applications [29]- [32], as well as a tool for algorithm evaluation and refinement of image and video codecs [33], [34].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Test pattern based evaluation of ringing and blur in JPEG and JPEG2000 compressed images

Punchihewa

Keerl

2010

2010 4th International Conference on Signal Processing and Communication Systems

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In this paper we present main parameters of the ringing artefact and demonstrate their influence on image fidelity of lossy compressed images. We apply variations of these parameters to create a specific set of test images to highlight the ringing artefact and to be able to evaluate the behaviour of ringing along edges. Therefore we introduce a new ringing metric, that analyzes image profiles of compressed images in terms of global luminance shift, blur and ringing.

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“…These deblurring techniques were proposed decades ago based on the MLE. However, they both have the disadvantage of being sensitive to any incorrect PSF estimate, and the ringing effect [33,34] is unavoidable. In recent years, the theory of sparse representation and machine learning have been introduced in the field of image restoration [35][36][37].…”

Section: The Proposed Learning-based Blur Removal Methodsmentioning

confidence: 99%

Efficient learning-based blur removal method based on sparse optimization for image restoration

Yang

Chen

et al. 2020

PLoS ONE

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In imaging systems, image blurs are a major source of degradation. This paper proposes a parameter estimation technique for linear motion blur, defocus blur, and atmospheric turbulence blur, and a nonlinear deconvolution algorithm based on sparse representation. Most blur removal techniques use image priors to estimate the point spread function (PSF); however, many common forms of image priors are unable to exploit local image information fully. In this paper, the proposed method does not require models of image priors. Further, it is capable of estimating the PSF accurately from a single input image. First, a blur feature in the image gradient domain is introduced, which has a positive correlation with the degree of blur. Next, the parameters for each blur type are estimated by a learning-based method using a general regression neural network. Finally, image restoration is performed using a half-quadratic optimization algorithm. Evaluation tests confirmed that the proposed method outperforms other similar methods and is suitable for dealing with motion blur in real-life applications. OPEN ACCESSCitation: Yang H, Su X, Chen S, Zhu W, Ju C (2020) Efficient learning-based blur removal method based on sparse optimization for image restoration. PLoS ONE 15(3): e0230619. https:// introduced a sparsity function with an 0 -norm constraint, while Pan et al. [6] estimated the blur kernel from the dark channel of the blurry images. These estimation techniques perform well when dealing with hand-drawn PSFs. However, in real-life situations, the blur models are often known. For example, when monitoring targets on a conveyor belt with a fixed camera, the PSF can be modeled based on the motion length during the exposure time. Similarly, with respect to space exploration, the atmospheric turbulence can be modeled by a Gaussian function, whose variance indicates the blur degree. Defocus blur can be modeled based on the defocus radius. It is more convenient and practical to solve a parameter identification problem than to estimate the PSF. Given this fact, Jalobeanu et al. [7] used the maximum likelihood estimator (MLE) on the entire dataset available to estimate the parameters for a Gaussian model. Yin and Hussain [8] combined the non-Gaussianity measures for independent component analysis to estimate the parameters for blur models. Dash and Majhi [9] suggested a radial basis function neural network with image features based on the magnitude of Fourier coefficients to estimate the motion lengths. Yan and Shao [10] proposed a supervised deep neural network to classify the blur type and adopted the expected patch log likelihood method [11] to restore the latent image. Further, Kumar et al. [12] used the Tchebycheff moment to estimate the Gaussian variance for turbulence blurs.With a known PSF, the latent image can be restored using inverse filters or some other nonblind deconvolution method. Levin et al. [13] proposed a hyper-Laplacian prior and adopted the iterative reweighted least squares (IRLS) algorithm to solve the optimi...

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Adaptive Image Deblurring with Ringing Control

Cited by 9 publications

References 6 publications

Edge quality metrics for image enhancement

Edge quality metrics for image enhancement

Test pattern based evaluation of ringing and blur in JPEG and JPEG2000 compressed images

Efficient learning-based blur removal method based on sparse optimization for image restoration

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