TY - GEN
T1 - Deep Texture and Structure Aware Filtering Network for Image Smoothing
AU - Lu, Kaiyue
AU - You, Shaodi
AU - Barnes, Nick
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Image smoothing is a fundamental task in computer vision, that attempts to retain salient structures and remove insignificant textures. In this paper, we aim to address the fundamental shortcomings of existing image smoothing methods, which cannot properly distinguish textures and structures with similar low-level appearance. While deep learning approaches have started to explore structure preservation through image smoothing, existing work does not yet properly address textures. To this end, we generate a large dataset by blending natural textures with clean structure-only images, and use this to build a texture prediction network (TPN) that predicts the location and magnitude of textures. We then combine the TPN with a semantic structure prediction network (SPN) so that the final texture and structure aware filtering network (TSAFN) is able to identify the textures to remove (“texture-awareness”) and the structures to preserve (“structure-awareness”). The proposed model is easy to understand and implement, and shows good performance on real images in the wild as well as our generated dataset.
AB - Image smoothing is a fundamental task in computer vision, that attempts to retain salient structures and remove insignificant textures. In this paper, we aim to address the fundamental shortcomings of existing image smoothing methods, which cannot properly distinguish textures and structures with similar low-level appearance. While deep learning approaches have started to explore structure preservation through image smoothing, existing work does not yet properly address textures. To this end, we generate a large dataset by blending natural textures with clean structure-only images, and use this to build a texture prediction network (TPN) that predicts the location and magnitude of textures. We then combine the TPN with a semantic structure prediction network (SPN) so that the final texture and structure aware filtering network (TSAFN) is able to identify the textures to remove (“texture-awareness”) and the structures to preserve (“structure-awareness”). The proposed model is easy to understand and implement, and shows good performance on real images in the wild as well as our generated dataset.
KW - Deep learning
KW - Image smoothing
KW - Texture prediction
UR - http://www.scopus.com/inward/record.url?scp=85055436088&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01225-0_14
DO - 10.1007/978-3-030-01225-0_14
M3 - Conference contribution
SN - 9783030012243
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 229
EP - 245
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Weiss, Yair
A2 - Hebert, Martial
PB - Springer Verlag
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -