@Article{NMTMA-10-4,
author = {},
title = {Learning Non-Negativity Constrained Variation for Image Denoising and Deblurring},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2017},
volume = {10},
number = {4},
pages = {852--871},
abstract = {<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>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.2017.m1653},
url = {https://global-sci.com/article/90525/learning-non-negativity-constrained-variation-for-image-denoising-and-deblurring}
}