A Perceptually Inspired Variational Framework for Color Enhancement
This work addresses the challenge of designing perceptually inspired color enhancement methods for image processing applications, but it appears incremental as it builds on existing models.
The paper tackles the problem of characterizing color correction algorithms by proposing a variational framework for color enhancement inspired by human color perception, resulting in explicit functionals and a method to reduce computational cost from O(N^2) to O(N log N).
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.