一个网络搞定六种图像增强，风格可切换。
Oneta: Multi-Style Image Enhancement Using Eigentransformation Functions
- 用特征变换函数和颜色矩阵分两步增强图像。
- 单个模型在30个数据集上完成6类增强任务。
- 支持6种风格，训练时用对应数据对学习参数。
本文提出首个针对多风格图像增强的新算法Oneta。该方法采用两个点运算器：先通过变换函数（TF）进行亮度增强，再利用颜色校正矩阵（CCM）进行色彩校正。这一两步模型虽简单，却达到高性能上限。引入特征变换函数（eigenTF）以紧凑形式表示TF。Oneta网络包含Y-Net和C-Net，分别预测eigenTF和CCM参数。为支持K种风格，模型使用K个可学习的风格令牌。训练时，每个风格令牌基于对应数据集的图像对进行学习；测试时，选择其中一个风格令牌对输入图像进行相应增强。大量实验表明，单个Oneta网络可有效完成六项增强任务——修饰、图像信号处理、低光照增强、去雾、水下图像增强及白平衡——覆盖30个数据集。
原文摘要 · Abstract (English)
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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