TY - JOUR
T1 - Learning Non-Negativity Constrained Variation for Image Denoising and Deblurring
AU - Tengda Wei, Linshan Wang, Ping Lin, Jialing Chen, Yangfan Wang & Haiyong Zheng
JO - Numerical Mathematics: Theory, Methods and Applications
VL - 4
SP - 852
EP - 871
PY - 2017
DA - 2017/10
SN - 10
DO - http://doi.org/10.4208/nmtma.2017.m1653
UR - https://global-sci.org/intro/article_detail/nmtma/10459.html
KW - Learning idea, TV-based model, constraint, ε-constraint method, image restoration.
AB - <p style="text-align: justify;">This paper presents a heuristic Learning-based Non-Negativity Constrained Variation (L-NNCV) aiming to search the coefficients of variational model automatically and make the variation adapt different images and problems by supervised-learning strategy. The model includes two terms: a problem-based term that is derived from the prior knowledge, and an image-driven regularization which is learned by some training samples. The model can be solved by classical ε-constraint method. Experimental results show that: the experimental effectiveness of each term in the regularization accords with the corresponding theoretical proof; the proposed method outperforms other PDE-based methods on image denoising and deblurring.</p>