TDCC: top-down semantic aggregation for colour constancy

• Published in: IET image processing Vol. 13; no. 11; pp. 1944 - 1950
• Main Authors: Li, Xiaoqiang, Zhu, Yaqin, Han, Jiayue, Li, Jide, Lian, Huicheng, Tong, Weiqin
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 19.09.2019
• Subjects: aggregation; CNN structure; convolutional neural nets; convolutional neural network; high‐level information propagation; image colour analysis; image representation; image restoration; Research Article; TDCC methods; top‐down semantic aggregation for colour constancy methods; CNN structure; image restoration; convolutional neural network; image representation; TDCC methods; aggregation; high-level information propagation; image colour analysis; convolutional neural nets; top-down semantic aggregation for colour constancy methods
• ISSN: 1751-9659; 1751-9667; 1751-9667
• DOI: 10.1049/iet-ipr.2019.0480

Images obtained from an illuminated scene often have their original colour contaminated. Colour constancy is a study considering how to restore them. Substantial progress on colour constancy has been made in recent years due to the development of a convolutional neural network (CNN). In a CNN structure, high-level features contain semantic information while low-level features show local details. If both are taken into account, they would help achieve a more accurate illuminant estimation. However, previous works paid little attention to the latter for lack of frameworks, which can combine those two kinds of features together. Inspired by the pyramid model, a top-down network that successively propagates high-level information to low-level layers is proposed. This network, named top-down semantic aggregation for colour constancy (TDCC), takes full advantage of the multi-scale representations with strong semantics. As a result, objects with intrinsic colours are captured and a better estimation is obtained. Experiments on three benchmark datasets demonstrate that TDCC significantly outperforms state-of-the-art colour constancy methods.