Oneta: Multi-Style Image Enhancement Using Eigentransformation Functions

• URL: http://arxiv.org/abs/2506.23547v1
• Date: Mon, 30 Jun 2025 06:36:11 GMT
• Title: Oneta: Multi-Style Image Enhancement Using Eigentransformation Functions
• Authors: Jiwon Kim, Soohyun Hwang, Dong-O Kim, Changsu Han, Min Kyu Park, Chang-Su Kim,
• Abstract summary: The first algorithm, called Oneta, for a novel task of multi-style image enhancement is proposed.<n>Oneta uses two point operators sequentially: intensity enhancement with a transformation function (TF) and color correction with a color correction matrix ( CCM)
• Score: 20.459710796298722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The first algorithm, called Oneta, for a novel task of multi-style image enhancement is proposed in this work. Oneta uses two point operators sequentially: intensity enhancement with a transformation function (TF) and color correction with a color correction matrix (CCM). This two-step enhancement model, though simple, achieves a high performance upper bound. Also, we introduce eigentransformation function (eigenTF) to represent TF compactly. The Oneta network comprises Y-Net and C-Net to predict eigenTF and CCM parameters, respectively. To support $K$ styles, Oneta employs $K$ learnable tokens. During training, each style token is learned using image pairs from the corresponding dataset. In testing, Oneta selects one of the $K$ style tokens to enhance an image accordingly. Extensive experiments show that the single Oneta network can effectively undertake six enhancement tasks -- retouching, image signal processing, low-light image enhancement, dehazing, underwater image enhancement, and white balancing -- across 30 datasets.

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