Abstract
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.
Abstract (translated)
在本文中，我们提出了一种神经网络预设置技术，以解决现有颜色风格传输方法的限制，包括视觉偏差、巨大的内存要求和缓慢的风格切换速度。我们的技术基于两个核心设计。首先，我们提议使用无监督神经网络颜色映射(DNCM)，通过图像自适应的颜色映射矩阵，对每个像素进行连续的操作，避免偏差并支持具有较小内存 footprint的高分辨率输入。其次，我们开发了一道两阶段的 pipeline，将任务分为颜色正常化和风格化，以便通过提取颜色风格作为预设置，并在正常化输入图像中重用它们，实现高效的风格切换。由于pairwise dataset 不存在，我们描述了如何通过自监督策略训练神经网络预设置。通过全面评估，我们展示了神经网络预设置比现有方法的各种优点。此外，我们展示，我们的训练模型自然地支持多个应用程序，包括暗光图像增强、水下图像修复、图像去雾和图像协调。
URL
https://arxiv.org/abs/2303.13511
