A Perceptually Inspired Variational Framework for Color Enhancement

Abstract

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, e.g., 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 O(N2) to O(N logN), being N the number of pixels of the input image.

An Algorithmic Analysis of Variational Models for Perceptual Local Contrast Enhancement

“…The code implements the algorithm presented by Palma-Amestoy et al [7], with a precomputation of the polynomials. These polynomials are inputted from text files which are also provided.…”

“…In [7] the authors built a general variational framework to perform perceptual color correction. Their work is based on the selection of energy functionals through suitable constraints related to phenomenological properties of the Human Visual System (HVS).…”

Duality Principle for Image Regularization and Perceptual Color Correction Models

“…Indeed, a straightforward computation shows that (λu k 0 + β/2)/(λ + β) is closer to 1/2 than u k 0 , ∀k ∈ {1, 2, 3}, meaning that the color cast of u 0 has been reduced (see [20] for details).…”

“…Regarding contrast modification, the pioneer variational approach is due to Sapiro and Caselles [23], who performed contrast enhancement for histogram equalization purpose. Since then, this variational formulation has been generalized and applied in different contexts: perceptual color correction [4], [20], tone mapping [14], and gamut mapping [25] to name a few. Connections have also been made between the variational formulation [4] and Retinex theory [5], [6].…”

“…The situation is even more tricky when dealing with the contrast modification variational models [4], [20] since they are not convex. The use of duality principle in convex optimization (see e.g.…”