A Perceptually Inspired Variational Framework for Color Enhancement

2025-11-28

AI Summary

This paper addresses the longstanding challenge in image color enhancement of simultaneously achieving visual naturalness and computational efficiency. We propose a human perception-inspired variational framework. Methodologically, we first systematically formulate three fundamental principles for perception-inspired energy functionals, then construct three theoretically grounded explicit functionals—respectively modeling color contrast, chromatic distribution dispersion, and perceptual consistency. The optimization is performed via gradient descent, augmented by a generic acceleration strategy based on the fast Fourier transform (FFT), reducing computational complexity from $O(N^2)$ to $O(N log N)$. Experiments demonstrate that our method significantly outperforms conventional approaches across diverse images: it enhances color contrast and distribution rationality while better preserving fine details and visual naturalness, all at substantially reduced computational cost.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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 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 ${cal O}(Nlog N)$, being $N$ the number of input pixels.

Problem

• Developing perceptually inspired variational color enhancement algorithms
• Characterizing model behavior for image contrast and dispersion
• Reducing computational cost from O(N^2) to O(N log N)

Innovation

• Variational formulation for color contrast enhancement
• Gradient descent to compute functional minima
• Methodology reduces computational cost to O(N log N)