Color Constancy based on Image Similarity via Bilayer Sparse Coding

TL;DR

This paper introduces a bilayer sparse coding model that improves color constancy by simultaneously considering low-level color distribution and high-level scene content, leading to more accurate illumination estimation.

Contribution

The paper presents a novel bilayer sparse coding approach that integrates both low-level and high-level image information for enhanced illumination estimation.

Findings

Outperforms existing illumination estimation methods.

Superior to combinational strategies in accuracy.

Effective on real-world image datasets.

Abstract

Computational color constancy is a very important topic in computer vision and has attracted many researchers' attention. Recently, lots of research has shown the effects of high level visual content information for illumination estimation. However, all of these existing methods are essentially combinational strategies in which image's content analysis is only used to guide the combination or selection from a variety of individual illumination estimation methods. In this paper, we propose a novel bilayer sparse coding model for illumination estimation that considers image similarity in terms of both low level color distribution and high level image scene content simultaneously. For the purpose, the image's scene content information is integrated with its color distribution to obtain optimal illumination estimation model. The experimental results on two real-world image sets show that our algorithm is superior to other prevailing illumination estimation methods, even better than combinational methods.