Blind Image Restoration Using Improved APEX Method with Pre-denoising

Yuan, Dong Jian; Min, Qi; Wang, Yi; Yang, Hao

doi:10.1109/icig.2007.55

Cited by 6 publications

(10 citation statements)

References 25 publications

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“…In the image degrading system, a PSF is generally viewed as probability density function as it is nonnegative and integrated to unity [7]. Similar to the property of PSF, Parzen-window estimate introduces a nonparametric method for estimating density function and it utilizes some given samples to estimate the probability distribution.…”

Section: Psf Estimationmentioning

confidence: 99%

Blind Image Restoration Using Divisional Regularization and Wavelet Technique

Chen

Guo

2008

2008 Fourth International Conference on Natural Computation

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Regularization method has been widely used in blind image restoration. Most regularization operators, however, are applied uniformly without considering difference of edge regions, which results in an unsolved trade-off conflict between smooth and edge regions. In this paper, we apply suitable regularization operators to smooth regions and edge regions respectively according to their characteristics instead of a global and constant one, and further employ the wavelet technique to control the noise amplification in order to improve the quality of divisional regularization. Experiment results show that our proposed Divisional Regularization and Wavelet Technique (DRWT) can deblur effectively without ringing effect in the restored images, thus improves edge restoration and reduces noise amplification.

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Section: Psf Estimationmentioning

confidence: 99%

Blind Image Restoration Using Divisional Regularization and Wavelet Technique

Chen

Guo

2008

2008 Fourth International Conference on Natural Computation

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“…Maximum Likelihood Estimation algorithm and the Richardson–Lucy algorithm (RL) [ 8 ] are the most representative. A multiplicative iterative approach (MIA) [ 9 , 10 ] was proposed based on a probabilistic model. MIA [ 9 , 10 ] naturally preserves the non-negative constraint on the iterative solutions when the initial estimates are non-negative, producing a restored image of high quality.…”

Section: Introductionmentioning

confidence: 99%

“…A multiplicative iterative approach (MIA) [ 9 , 10 ] was proposed based on a probabilistic model. MIA [ 9 , 10 ] naturally preserves the non-negative constraint on the iterative solutions when the initial estimates are non-negative, producing a restored image of high quality. At present, neural networks are the most popular in computer vision.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this paper, an efficient scheme for blind deblurring is introduced via the sigmoid function, which was inspired by the multiplicative iterative algorithm (MIA) [ 9 , 10 ]. The MIA, as reported in [ 9 , 10 ], is efficient but limited to weak degradation. To overcome this drawback and deal with the severe degradation problem, a new form of iteration strategy is adopted in this work, which employs the sigmoid function, leading to a novel blind deconvolution algorithm for restoration of seriously degraded and blurred images.…”

Section: Introductionmentioning

confidence: 99%

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Blind Deblurring Based on Sigmoid Function

Sun

Duan

et al. 2021

Sensors

Self Cite

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Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.

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Midfrequency-based real-time blind image restoration via independent component analysis and genetic algorithms

Luo

2011

Opt. Eng

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Blind Image Restoration Using Improved APEX Method with Pre-denoising

Cited by 6 publications

References 25 publications

Blind Image Restoration Using Divisional Regularization and Wavelet Technique

Blind Image Restoration Using Divisional Regularization and Wavelet Technique

Blind Deblurring Based on Sigmoid Function

Midfrequency-based real-time blind image restoration via independent component analysis and genetic algorithms

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