In this paper, we present a Neural Preset technique to address the
limitations of existing color style transfer methods, including visual
artifacts, vast memory requirement, and slow style switching speed. Our method
is based on two core designs. First, we propose Deterministic Neural Color
Mapping (DNCM) to consistently operate on each pixel via an image-adaptive
color mapping matrix, avoiding artifacts and supporting high-resolution inputs
with a small memory footprint. Second, we develop a two-stage pipeline by
dividing the task into color normalization and stylization, which allows
efficient style switching by extracting color styles as presets and reusing
them on normalized input images. Due to the unavailability of pairwise
datasets, we describe how to train Neural Preset via a self-supervised
strategy. Various advantages of Neural Preset over existing methods are
demonstrated through comprehensive evaluations. Besides, we show that our
trained model can naturally support multiple applications without fine-tuning,
including low-light image enhancement, underwater image correction, image
dehazing, and image harmonization.

本文提出了一种神经预置技术，即基于 DNCM 和二阶段管道的自我监督策略，解决了基于颜色风格转换技术的局限性，包括视觉伪影、大量内存需求和风格转换速度缓慢等问题，并在多个领域取得了优异的表现。