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논문 기본 정보

자료유형
학술저널
저자정보
Soniya James (The Oxford College of Engineering) Vivek Maik (The Oxford College of Engineering) K. Karibassappa (The Oxford College of Engineering) Joonki Paik (Chung Ang University)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.4 No.6
발행연도
2015.12
수록면
413 - 421 (9page)

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초록· 키워드

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Regularized Blind Deconvolution is a problem applicable in degraded images in order to bring the original image out of blur. Multichannel blind Deconvolution considered as an optimization problem. Each step in the optimization is considered as variable splitting problem using an algorithm called Alternating Minimization Algorithm. Each Step in the Variable splitting undergoes Augmented Lagrangian method (ALM) / Bregman Iterative method. Regularization is used where an ill posed problem converted into a well posed problem. Two well known regularizers are Tikhonov class and Total Variation (TV) /L2 model. TV can be isotropic and anisotropic, where isotropic for L2 norm and anisotropic for L1 norm. Based on many probabilistic model and Fourier Transforms Image deblurring can be solved. Here in this paper to improve the performance, we have used an adaptive regularization filtering and isotropic TV model Lp norm. Image deblurring is applicable in the areas such as medical image sensing, astrophotography, traffic signal monitoring, remote sensors, case investigation and even images that are taken using a digital camera /mobile cameras.

목차

Abstract
1. Introduction
2. Adaptive Regularization
3. Ill posed problem
4.OPTIMIZATION PROBLEM
5. Non Blind deconvolution for Image Estimation
6. Application
7. Astronomical Image Deblurring
8. Medical Image Deblurring
9 RESULTS
10. CONCLUSION
References

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UCI(KEPA) : I410-ECN-0101-2016-569-002214856