Basic phenomenology of human color vision has been widely taken as an inspiration to devise explicit color correction algorithms. The behavior of these models in terms of significative image features (such as contrast and dispersion) can be difficult to characterize. To cope with this, we propose to use a variational formulation of color contrast enhancement that is inspired by the basic phenomenology of color perception. In particular, we devise a set of basic requirements to be fulfilled by an energy to be considered as `perceptually inspired', showing that there is an explicit class of functionals satisfying all of them. We single out three explicit functionals that we consider of basic interest, showing similarities and differences with existing models. The minima of such functionals is computed using a gradient descent approach. We also present a general methodology to reduce the computational cost of the algorithms under analysis from ${\cal O}(N^2)$ to ${\cal O}(N\log N)$, being $N$ the number of input pixels.

翻译：人类色彩视觉的基本现象学已被广泛用作设计显式色彩校正算法的灵感来源。这些模型在显著图像特征（如对比度和离散度）方面的行为可能难以表征。为解决这一问题，我们提出一种受色彩感知基本现象学启发的色彩对比度增强变分公式。具体而言，我们设计了一套基本要求，使能量泛函满足‘感知启发’的标准，并证明存在一个显式泛函类满足所有要求。我们分离出三个具有基础研究价值的显式泛函，展示了其与现有模型的异同。此类泛函的最小值通过梯度下降法计算。我们还提出一种通用方法，将所分析算法的计算复杂度从 ${\\cal O}(N^2)$ 降低至 ${\\cal O}(N\\log N)$，其中 $N$ 为输入像素数量。