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:在本文中,我们介绍了一种神经预设技术来解决现有颜色风格转换方法的限制,包括视觉伪影、大量内存需求和慢速风格切换速度等。我们的方法基于两个核心设计。首先,我们提出了确定性神经颜色映射(DNCM)来通过一个图像自适应颜色映射矩阵一致地在每个像素上操作,避免伪影并支持具有小内存占用的高分辨率输入。其次,我们通过将任务分成彩色标准化和风格化两个阶段来开发一个两阶段流水线,允许通过提取颜色风格作为预设并在标准化的输入图像上重用它们来进行有效的风格切换。由于成对数据集的不可用性,我们描述了如何通过自我监督策略来训练神经预设。通过全面的评估展示了神经预设相比现有方法的各种优势。此外,我们展示了我们训练的模型可以在没有微调的情况下自然地支持多种应用,包括低光照图像增强、水下图像校正、图像去雾和图像协调。