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<p>The human visual system achieves color constancy, allowing consistent color perception under varying environmental contexts, while also being deceived by color illusions, where contextual information affects our perception. Despite the close relationship between color constancy and color illusions, and their potential benefits to the field, both phenomena are rarely studied together in computer vision. In this study, we present the benefits of considering color illusions in the field of computer vision. Particularly, we introduce a learning-free method, namely&#xa0;<i>multiresolution color constancy</i>, which combines insights from computational neuroscience and computer vision to address both phenomena within a single framework. Our approach performs color constancy in both multi- and single-illuminant scenarios, while it is also deceived by assimilation illusions. Additionally, we extend our method to low-light image enhancement, thus, demonstrate its usability across different computer vision tasks. Through comprehensive experiments on color constancy, we show the effectiveness of our method in multi-illuminant and single-illuminant scenarios. Furthermore, we compare our method with state-of-the-art learning-based models on low-light image enhancement, where it shows competitive performance. This work presents the first method that integrates color constancy, color illusions, and low-light image enhancement in a single and explainable framework.</p>

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A Traditional Approach for Color Constancy and Color Assimilation Illusions with Its Applications to Low-Light Image Enhancement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月6日

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  • Oguzhan Ulucan,
  • Diclehan Ulucan,
  • Marc Ebner

摘要

The human visual system achieves color constancy, allowing consistent color perception under varying environmental contexts, while also being deceived by color illusions, where contextual information affects our perception. Despite the close relationship between color constancy and color illusions, and their potential benefits to the field, both phenomena are rarely studied together in computer vision. In this study, we present the benefits of considering color illusions in the field of computer vision. Particularly, we introduce a learning-free method, namely multiresolution color constancy, which combines insights from computational neuroscience and computer vision to address both phenomena within a single framework. Our approach performs color constancy in both multi- and single-illuminant scenarios, while it is also deceived by assimilation illusions. Additionally, we extend our method to low-light image enhancement, thus, demonstrate its usability across different computer vision tasks. Through comprehensive experiments on color constancy, we show the effectiveness of our method in multi-illuminant and single-illuminant scenarios. Furthermore, we compare our method with state-of-the-art learning-based models on low-light image enhancement, where it shows competitive performance. This work presents the first method that integrates color constancy, color illusions, and low-light image enhancement in a single and explainable framework.