Zero norm based analysis model for image smoothing and reconstruction. (15th October 2020)

• Record Type: Journal Article
• Authors: Song, Jiebo; Li, Jia; Yao, Zhengan; Ma, Kaisheng; Bao, Chenglong
• Is Part Of: Inverse problems. Volume 36:Number 11(2020)
• Publication Date: 2020-10-15
• Subjects: L0 optimization -- image smoothing and restoration -- sparse view CT imaging; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/abb87e
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: The sparsity-based approaches have demonstrated promising performance in image processing. In this paper, for better preservation of the salient edge structures of images, we propose an ℓ 0 + ℓ 2 -norm based analysis model， which requires solving a challenging non-separable ℓ 0 -norm related minimization problem, and we also propose an inexact augmented Lagrangian method with proven convergence to a local minimum. Extensive experiments in image smoothing, including texture removal and context smoothing, show that our method achieves better visual results over various sparsity-based models and the CNN method. Also, experiments on sparse view CT reconstruction further validate the advantage of the proposed method.