Abstract
Deep learning-based bilateral grid processing has emerged as a promising solution for image enhancement, inherently encoding spatial and intensity information while enabling efficient full-resolution processing through slicing operations. However, existing approaches are limited to linear affine transformations, hindering their ability to model complex color relationships. Meanwhile, while multi-layer perceptrons (MLPs) excel at non-linear mappings, traditional MLP-based methods employ globally shared parameters, which is hard to deal with localized variations. To overcome these dual challenges, we propose a Bilateral Grid-based Pixel-Adaptive Multi-layer Perceptron (BPAM) framework. Our approach synergizes the spatial modeling of bilateral grids with the non-linear capabilities of MLPs. Specifically, we generate bilateral grids containing MLP parameters, where each pixel dynamically retrieves its unique transformation parameters and obtain a distinct MLP for color mapping based on spatial coordinates and intensity values. In addition, we propose a novel grid decomposition strategy that categorizes MLP parameters into distinct types stored in separate subgrids. Multi-channel guidance maps are used to extract category-specific parameters from corresponding subgrids, ensuring effective utilization of color information during slicing while guiding precise parameter generation. Extensive experiments on public datasets demonstrate that our method outperforms state-of-the-art methods in performance while maintaining real-time processing capabilities.
Abstract (translated)
基于深度学习的双边网格处理已经成为图像增强的一个有前途的解决方案，它能够内在地编码空间和强度信息，并通过切片操作实现高效的全分辨率处理。然而，现有的方法仅限于线性仿射变换，这限制了它们建模复杂色彩关系的能力。同时，尽管多层感知机（MLPs）在非线性映射方面表现出色，但传统基于MLP的方法采用全局共享参数的方式难以应对局部变化。为克服这些双重挑战，我们提出了一种基于双边网格的像素自适应多层感知机（BPAM）框架。我们的方法将双边网格的空间建模能力和MLPs的非线性能力结合起来。具体来说，我们在生成的双边网格中包含MLP参数，每个像素根据其空间坐标和强度值动态检索独特的变换参数，并获得用于色彩映射的不同MLP。此外，我们提出了一种新颖的网格分解策略，将MLP参数分类为不同的类型并存储在单独的子网格中。多通道引导图被用来从相应的子网格中提取特定类别的参数，在切片过程中有效利用颜色信息的同时指导精确参数生成。在公共数据集上的广泛实验表明，我们的方法不仅在性能上超越了现有的最佳方法，还能保持实时处理能力。
URL
https://arxiv.org/abs/2507.12135
