3D lookup tables (3D LUTs) are a key component for image enhancement. Modern
image signal processors (ISPs) have dedicated support for these as part of the
camera rendering pipeline. Cameras typically provide multiple options for
picture styles, where each style is usually obtained by applying a unique
handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are
notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is
required. For this reason and other implementation limitations, their use on
mobile devices is less popular. In this work, we propose a Neural Implicit LUT
(NILUT), an implicitly defined continuous 3D color transformation parameterized
by a neural network. We show that NILUTs are capable of accurately emulating
real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles
into a single network with the ability to blend styles implicitly. Our novel
approach is memory-efficient, controllable and can complement previous methods,
including learned ISPs. Code, models and dataset available at:
this https URL

这项工作提出了一种神经隐式 LUT（NILUT），作为相机渲染管道的一部分，用于快速而低成本地进行多种图片风格的连续 3D 颜色转换，较传统的 3D LUT 方法更具有内存效率。

NILUT: 条件神经隐式 3D 查找表用于图像增强

NILUT: Conditional Neural Implicit 3D Lookup Tables for Image  Enhancement

Recently, deep learning-based image enhancement algorithms achieved
state-of-the-art (SOTA) performance on several publicly available datasets.
However, most existing methods fail to meet practical requirements either for
visual perception or for computation efficiency, especially for high-resolution
images. In this paper, we propose a novel real-time image enhancer via
learnable spatial-aware 3-dimentional lookup tables(3D LUTs), which well
considers global scenario and local spatial information. Specifically, we
introduce a light weight two-head weight predictor that has two outputs. One is
a 1D weight vector used for image-level scenario adaptation, the other is a 3D
weight map aimed for pixel-wise category fusion. We learn the spatial-aware 3D
LUTs and fuse them according to the aforementioned weights in an end-to-end
manner. The fused LUT is then used to transform the source image into the
target tone in an efficient way. Extensive results show that our model
outperforms SOTA image enhancement methods on public datasets both subjectively
and objectively, and that our model only takes about 4ms to process a 4K
resolution image on one NVIDIA V100 GPU.

本文提出了一种基于可学习的空间感知三维查找表（3D LUTs）的实时图像增强器，它充分考虑了全局情况和局部空间信息，通过在端到端的方式中的权重融合学习 3D LUT 并将其用于以有效的方式将源图像转换为目标色调，该模型在公共数据集上主观上和客观上均优于 SOTA 图像增强方法，并且仅使用一个 NVIDIA V100 GPU 即可在 4ms 内处理 4K 分辨率图像。

可学习的空间感知三维查找表实时图像增强器

Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables

Recent years have witnessed the increasing popularity of learning based
methods to enhance the color and tone of photos. However, many existing photo
enhancement methods either deliver unsatisfactory results or consume too much
computational and memory resources, hindering their application to
high-resolution images (usually with more than 12 megapixels) in practice. In
this paper, we learn image-adaptive 3-dimensional lookup tables (3D LUTs) to
achieve fast and robust photo enhancement. 3D LUTs are widely used for
manipulating color and tone of photos, but they are usually manually tuned and
fixed in camera imaging pipeline or photo editing tools. We, for the first time
to our best knowledge, propose to learn 3D LUTs from annotated data using
pairwise or unpaired learning. More importantly, our learned 3D LUT is
image-adaptive for flexible photo enhancement. We learn multiple basis 3D LUTs
and a small convolutional neural network (CNN) simultaneously in an end-to-end
manner. The small CNN works on the down-sampled version of the input image to
predict content-dependent weights to fuse the multiple basis 3D LUTs into an
image-adaptive one, which is employed to transform the color and tone of source
images efficiently. Our model contains less than 600K parameters and takes less
than 2 ms to process an image of 4K resolution using one Titan RTX GPU. While
being highly efficient, our model also outperforms the state-of-the-art photo
enhancement methods by a large margin in terms of PSNR, SSIM and a color
difference metric on two publically available benchmark datasets.

该论文介绍了一种学习图像自适应 3D 查找表以实现快速和强大的照片增强方法，该方法学习多个 3D LUTs 基础，同时使用小型卷积神经网络 (CNN) 以内容相关的方式自适应的融合多个基础 3D LUTs 并处理 4K 的图像仅需不到 2 毫秒。在性能和计算效率方面超越了现有的照片增强方法。