2013/08/31 by Ryan Wen Liu, Liu, Ryan Wen, Tian Xu +1
Computer Science · Engineering · #65K10 #68U10 #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #G.1.6 #I.4.4 #Image and Signal Denoising Methods #Sparse and Compressive Sensing Techniques #acm:65K10 #acm:68U10 #cs.CV #msc:65K10 #msc:68U10
paper · pdf · doi:10.48550/arxiv.1309.0123
4 pages, 5 figures
openalex publication_date 2013/08/31 · arxiv created 2013/10/02 · arxiv updated 2013/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, a new constrained hybrid variational deblurring model is developed by combining the non-convex first- and second-order total variation regularizers. Moreover, a box constraint is imposed on the proposed model to guarantee high deblurring performance. The developed constrained hybrid variational model could achieve a good balance between preserving image details and alleviating ringing artifacts. In what follows, we present the corresponding numerical solution by employing an iteratively reweighted algorithm based on alternating direction method of multipliers. The experimental results demonstrate the superior performance of the proposed method in terms of quantitative and qualitative image quality assessments.