title: Dictionary learning approach for image deconvolution with variance estimation.
abstract: In this paper, we propose a new dictionary learning approach for image deconvolution, which effectively integrates the Fourier regularization and dictionary learning technique into the deconvolution framework. Specifically, we propose an iterative algorithm with the decoupling of the deblurring and denoising steps in the restoration process. In the deblurring step, we involve a regularized inversion of the blur in the Fourier domain. Then we remove the colored noise using a dictionary learning method in the denoising step. In the denoising step, we propose an approach to update the estimation of noise variance for dictionary learning. We will show that this approach outperforms several state-of-the-art image deconvolution methods in terms of improvement in signal-to-noise ratio and visual quality.
authors.forename: Hang
authors.initials: H
authors.lastname: Yang
authors.forename: Ming
authors.initials: M
authors.lastname: Zhu
authors.forename: Xiaotian
authors.initials: X
authors.lastname: Wu
authors.forename: Zhongbo
authors.initials: Z
authors.lastname: Zhang
authors.forename: Heyan
authors.initials: H
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D016477:Artifacts; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D005583:Fourier Analysis; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity