研究目的
To develop a colour constancy algorithm for images of scenes lit by non-uniform light sources, aiming to remove colour cast and improve colour accuracy.
研究成果
The proposed CCAFIS algorithm effectively handles colour constancy in non-uniformly lit scenes by segmenting images, selecting segments with sufficient colour variation, and fusing correction factors. It outperforms existing statistical-based methods and is competitive with non-statistical methods, as demonstrated by lower angular errors and higher MOS scores on benchmark datasets.
研究不足
The method relies on empirically determined thresholds for NAAD, which may not generalize well to all image types. It is primarily designed for non-uniformly lit scenes and may not perform optimally with uniform lighting or very specific image characteristics. Computational complexity of K-means++ and fusion steps could be a limitation for real-time applications.
1：Experimental Design and Method Selection:
The method uses a histogram-based algorithm to determine the number of colour regions, applies K-means++ clustering for segmentation, computes NAAD for segment selection, and fuses initial colour constancy factors using Euclidean distances.
2：Sample Selection and Data Sources:
Benchmark image datasets including Multiple Light Source (MLS), MIMO, Gehler and Shi, Grey Ball, and UPenn Natural Image datasets are used.
3：List of Experimental Equipment and Materials:
No specific equipment or materials are mentioned; the method is computational and uses standard image processing techniques.
4：Experimental Procedures and Operational Workflow:
Convert RGB to grey image, create histogram, smooth it, count local maxima, apply K-means++ for segmentation, compute NAAD for each segment, compare to thresholds, calculate initial factors using Grey World method, fuse factors for each pixel using Euclidean distances, and apply Von-Kries Diagonal model for colour balancing.
5：Data Analysis Methods:
Performance is assessed using angular error (objective) and Mean Opinion Score (MOS) (subjective) metrics.
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