Paper
31 July 2023 CCMMF: color constancy method based on mobileViT and multi-scale fusion
Xiaoyu Zheng, Xuming Lu
Author Affiliations +
Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 127470A (2023) https://doi.org/10.1117/12.2689475
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China
Abstract
In recent years, color constancy has made great progress due to the application of convolutional neural networks (CNNs). However, CNNs cannot extract global features efficiently, and the use of only high-level features for estimation results in a large degree of information loss. In addition, the model based on CNN used for color constancy is not lightweight enough for application. To overcome those problems, a color constancy method based on MobileViT and multi-scale fusion is proposed. The method adopts the improved network structure based on MobileViT as the feature extraction module. It can make the extraction of local features and global representations simultaneously, with a complexity-reduced model. In further, a novel bidirectional multi-scale fusion network with two paths is presented to facilitate feature fusion between different levels. It makes full use of the multi-scale representation with strong semantics. As a result, more accurate illumination estimation is obtained. Experimental results show that compared with other methods, the proposed method has better comprehensive performance. The illumination estimation error of this method reaches the state-of-art level. With the size of parameters 1.24 MB, it is much lighter than mainstream networks.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaoyu Zheng and Xuming Lu "CCMMF: color constancy method based on mobileViT and multi-scale fusion", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 127470A (31 July 2023); https://doi.org/10.1117/12.2689475
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KEYWORDS
Feature fusion

Feature extraction

Transformers

Computer vision technology

Visualization

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