TY - GEN
T1 - Convex dictionary learning for single image super-resolution
AU - Ding, Pak Lun Kevin
AU - Li, Baoxin
AU - Chang, Kan
N1 - Funding Information:
†Efforts partially supported by the Natural Science Foundation of China via Grant 61401108 and also by Natural Science Foundation of Guangxi via Grant 2016GXNSFAA380154.
Funding Information:
∗Efforts partially supported by an ONR grant. The views/conclusions are solely of the authors and do not necessarily reflect ONR’s opinion.
PY - 2018/2/20
Y1 - 2018/2/20
N2 - In recent years, dictionary learning approaches have been used in image super-resolution, achieving promising results. Such approaches train a dictionary from image patches and reconstruct a new patch by sparse combination of the atoms of the dictionary. Typical training methods do not constrain the dictionary atoms. In this paper, we propose a convex dictionary learning (CDL) algorithm by constraining the dictionary atoms to be formed by non-negative linear combination of the training data, which is a natural, desired property. We evaluate our approach by demonstrating its performance gain over typical approaches.
AB - In recent years, dictionary learning approaches have been used in image super-resolution, achieving promising results. Such approaches train a dictionary from image patches and reconstruct a new patch by sparse combination of the atoms of the dictionary. Typical training methods do not constrain the dictionary atoms. In this paper, we propose a convex dictionary learning (CDL) algorithm by constraining the dictionary atoms to be formed by non-negative linear combination of the training data, which is a natural, desired property. We evaluate our approach by demonstrating its performance gain over typical approaches.
KW - Dictionary learning
KW - Sparse representation
KW - Super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85045324615&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045324615&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2017.8297045
DO - 10.1109/ICIP.2017.8297045
M3 - Conference contribution
AN - SCOPUS:85045324615
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 4058
EP - 4062
BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PB - IEEE Computer Society
T2 - 24th IEEE International Conference on Image Processing, ICIP 2017
Y2 - 17 September 2017 through 20 September 2017
ER -