基于人眼色彩感知机制，提出可高效计算的色彩增强新框架。
A Perceptually Inspired Variational Framework for Color Enhancement
- 以人眼感知规律为依据设计能量函数，确保增强效果符合视觉直觉。
- 三种具体函数实现色彩对比度提升，计算效率从O(N²)优化至O(N log N)。
- 适合图像处理、计算机视觉领域研究者，尤其关注感知质量提升者。
人类色彩视觉的基本现象被广泛用于启发显式色彩校正算法的设计。然而，这些模型在显著图像特征（如对比度和分布）方面的表现难以准确刻画。为此，我们提出一种受色彩感知基本原理启发的变分色彩对比度增强框架。具体而言，我们定义了一组基本要求，用以判断能量函数是否具备‘感知启发性’，并证明存在一个明确的函数类满足所有条件。我们从中选出三个具有基础意义的显式函数，并与现有模型进行比较，揭示其异同。通过梯度下降法求解这些函数的极小值。此外，我们还提出一种通用方法，将算法的计算复杂度从O(N²)降低至O(N log N)，其中N为输入像素数。
原文摘要 · Abstract (English)
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.
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