Oneta: Multi-Style Image Enhancement Using Eigentransformation Functions
This addresses the problem of applying multiple image enhancement styles with a single network for computer vision practitioners, though it appears incremental as it builds on existing transformation and correction techniques.
The authors introduced Oneta, the first algorithm for multi-style image enhancement, which uses a two-step model with intensity enhancement and color correction to handle six different enhancement tasks across 30 datasets, achieving a high performance upper bound.
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.