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.
Abstract (translated)
本文提出了一种名为Oneta的新颖的多风格图像增强任务算法。Oneta依次使用两个点操作器：一个是强度增强，通过转换函数（TF）进行；另一个是颜色校正，采用色彩校正矩阵（CCM）。尽管这种两步增强模型很简单，但它能够达到很高的性能上限。此外，我们引入了特征变换函数(eigenTF)来紧凑地表示TF。Oneta网络包括Y-Net和C-Net两个部分，分别用于预测eigenTF和CCM参数。为了支持K种风格，Oneta使用了K个可学习的标记(token)。在训练过程中，每个风格标签通过来自相应数据集的图像对进行学习。而在测试阶段，Oneta会选择一个K种风格标记中的其中一个来增强相应的图像。广泛的实验表明，单一的Oneta网络能够有效地完成六项增强任务——润饰、图像信号处理、低光环境下的图片增强、去雾、水下图片增强和白平衡调整，在30个数据集中表现出色。
URL
https://arxiv.org/abs/2506.23547
