Oneta: Multi-Style Image Enhancement Using Eigentransformation Functions

TL;DR

Oneta introduces a multi-style image enhancement method using eigentransformation functions and a dual-network architecture, enabling effective enhancement across diverse tasks and styles with a compact representation.

Contribution

The paper presents a novel multi-style image enhancement algorithm employing eigentransformation functions and learnable style tokens, achieving high performance across multiple enhancement tasks.

Findings

Effective multi-style enhancement across six tasks.

Supports 30 diverse datasets with high performance.

Uses eigentransformation functions for compact TF representation.

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.