Image Enhancement Based on Pigment Representation

2025-10-03

AI Summary

Existing image enhancement methods are constrained by fixed, predefined color spaces (e.g., RGB), lacking content adaptivity and thus struggling to balance enhancement quality and computational efficiency. To address this, we propose a novel image enhancement framework based on learnable, high-dimensional pigment representations. Our method first employs a vision encoder to predict content-adaptive pigment transformation and reprojection parameters; then decomposes the RGB input into high-dimensional pigment features, performs pigment-wise reprojection and fusion; and finally reconstructs the enhanced result via inverse transformation to RGB space. This work pioneers the modeling of pigment representation as a learnable, high-dimensional, content-driven feature space—breaking free from conventional color space limitations. Extensive experiments demonstrate state-of-the-art performance on image retouching and tone mapping tasks, while maintaining low computational overhead and a lightweight architecture (<1.2M parameters).

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This paper presents a novel and efficient image enhancement method based on pigment representation. Unlike conventional methods where the color transformation is restricted to pre-defined color spaces like RGB, our method dynamically adapts to input content by transforming RGB colors into a high-dimensional feature space referred to as extit{pigments}. The proposed pigment representation offers adaptability and expressiveness, achieving superior image enhancement performance. The proposed method involves transforming input RGB colors into high-dimensional pigments, which are then reprojected individually and blended to refine and aggregate the information of the colors in pigment spaces. Those pigments are then transformed back into RGB colors to generate an enhanced output image. The transformation and reprojection parameters are derived from the visual encoder which adaptively estimates such parameters based on the content in the input image. Extensive experimental results demonstrate the superior performance of the proposed method over state-of-the-art methods in image enhancement tasks, including image retouching and tone mapping, while maintaining relatively low computational complexity and small model size.

Problem

• Dynamic color transformation using high-dimensional pigment representation for images
• Adaptive image enhancement method surpassing conventional RGB space limitations
• Achieving superior retouching and tone mapping with low computational complexity

Innovation

• Transforms RGB colors into high-dimensional pigment representation
• Adaptively estimates parameters using a visual encoder
• Reprojects and blends pigments for enhanced output