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.

翻译：本文提出了一种神经预设技术，以解决现有色彩风格迁移方法存在的视觉伪影、内存需求大及风格切换速度慢等问题。该方法基于两项核心设计：首先，提出确定性神经色彩映射（DNCM），通过图像自适应色彩映射矩阵对每个像素进行一致性处理，在避免伪影的同时支持高分辨率输入且内存占用极小；其次，构建两阶段流水线，将任务分解为色彩归一化与风格化，通过将色彩风格提取为预设并复用于归一化输入图像，实现高效的风格切换。针对成对数据集缺乏的问题，我们阐述了如何通过自监督策略训练神经预设模型。综合评估表明，神经预设相比现有方法具有多方面优势。此外，该训练模型无需微调即可天然支持低光图像增强、水下图像校正、图像去雾及图像协调等多种应用场景。