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Single image blind deblurring via adapting total generalized variation
- Source :
- CISP-BMEI
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- This paper introduces a fast blind deconvolution strategy for image deblurring by modifying a recent natural image model, i.e., the total generalized variation (TGV), which aims at reconstructing an image with higher-order smoothness as well as sharp edges. But, when it turns to the blind issue, as demonstrated either empirically or theoretically by a few previous blind deblurring works, natural image models including TGV are more often than not inclined to trivial solutions, e.g., the delta blur kernel and the input blurred images. Inspired by the discovery, a simple, yet effective modifying strategy is applied to the second-order TGV, leading to a novel l 0 –l 1 -norm-based image regularization adaptable to the blind deblurring problem. Then, a numerical algorithm is deduced with O(NlogN) complexity, via borrowing ideas of the operator splitting method, the augmented Lagrangian, and also the fast Fourier transform (FFT). Experimental results on both synthetic and real color blurred images demonstrate the superiority or comparable performance of the new approach to state-of-the-art ones, in terms of deblurring accuracy and efficiency.
- Subjects :
- Blind deconvolution
Deblurring
business.industry
Computer science
Augmented Lagrangian method
Fast Fourier transform
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
020206 networking & telecommunications
02 engineering and technology
Kernel (image processing)
Total generalized variation
Computer Science::Computer Vision and Pattern Recognition
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer vision
Artificial intelligence
Deconvolution
business
Image restoration
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
- Accession number :
- edsair.doi...........9349e050193e8f362f621e3d3c234894