Oneta: Multi-Style Image Enhancement Using Eigentransformation Functions
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909666892578816 |
|---|---|
| author | Kim, Jiwon Hwang, Soohyun Kim, Dong-O Han, Changsu Park, Min Kyu Kim, Chang-Su |
| author_facet | Kim, Jiwon Hwang, Soohyun Kim, Dong-O Han, Changsu Park, Min Kyu Kim, Chang-Su |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Oneta: Multi-Style Image Enhancement Using Eigentransformation Functions Kim, Jiwon Hwang, Soohyun Kim, Dong-O Han, Changsu Park, Min Kyu Kim, Chang-Su Computer Vision and Pattern Recognition 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. |
| title | Oneta: Multi-Style Image Enhancement Using Eigentransformation Functions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.23547 |