Abstract
Images taken in low light often show color shift, low contrast, noise, and other artifacts that hurt computer-vision accuracy. Retinex theory addresses this by viewing an image S as the pixel-wise product of reflectance R and illumination I, mirroring the way people perceive stable object colors under changing light. The decomposition is ill-posed, and classic Retinex models have four key flaws: (i) they treat the red, green, and blue channels independently; (ii) they lack a neuroscientific model of color vision; (iii) they cannot perfectly rebuild the input image; and (iv) they do not explain human color constancy. We introduce the first Quaternion Retinex formulation, in which the scene is written as the Hamilton product of quaternion-valued reflectance and illumination. To gauge how well reflectance stays invariant, we propose the Reflectance Consistency Index. Tests on low-light crack inspection, face detection under varied lighting, and infrared-visible fusion show gains of 2-11 percent over leading methods, with better color fidelity, lower noise, and higher reflectance stability.
Abstract (translated)
在低光条件下拍摄的照片通常会出现色偏、对比度低、噪点和其他影响计算机视觉准确性的伪像。Retinex理论通过将图像S视为反射率R和照明I的逐像素乘积来解决这些问题，这种模型反映了人们在不同光照条件下感知稳定物体颜色的方式。然而，这种分解是不适定问题（ill-posed problem），经典的Retinex模型存在四个主要缺陷：(i) 它们独立处理红色、绿色和蓝色通道；(ii) 缺乏神经科学色彩视觉模型；(iii) 无法完美重建输入图像；以及(iv) 不能解释人类的色恒常性。 我们引入了首个四元数Retinex公式，在该公式中，场景被描述为四元数值反射率与照明的汉密尔顿积。为了评估反射率保持不变的程度，我们提出了反射一致性指标（Reflectance Consistency Index）。在低光裂纹检测、不同光照条件下的面部识别以及红外和可见光图像融合方面的测试显示，相较于领先的方法，我们的方法提高了2-11%的性能，在色彩保真度、噪声降低和反射率稳定性方面表现更优。
URL
https://arxiv.org/abs/2507.16683
