通过Clifford信息融合的实时方法实现UHD低光照图像增强
UHD Low-Light Image Enhancement via Real-Time Enhancement Methods with Clifford Information Fusion
AI总结：
本文提出基于Clifford代数的实时UHD低光照图像增强网络，通过几何特征融合解决内存瓶颈问题，实现4K/8K图像毫秒级推理，并在多个恢复指标上优于现有方法。
摘要：
考虑到效率，超高清（UHD）低光照图像恢复极具挑战性。现有基于Transformer架构或高维复数卷积神经网络的方法常受内存瓶颈限制，难以在边缘设备上实现毫秒级推理。为解决此问题，我们提出了一种基于二维欧几里得空间中Clifford代数的几何特征融合的新型实时UHD低光照增强网络。首先，我们构建了一个四层特征金字塔，逐步增加分辨率，通过高斯模糊核将输入图像分解为低频和高频结构成分，并采用基于深度可分离卷积的轻量级U-Net进行双分支特征提取。其次，为解决传统高低频特征融合中的结构信息丢失和伪影问题，我们引入了空间感知的Clifford代数，将特征张量映射到多向量空间（标量、向量、双矢量），并通过Clifford相似性聚合特征，同时抑制噪声并保留纹理。在重建阶段，网络输出自适应Gamma和Gain映射，通过Retinex理论进行物理约束的非线性亮度调整。结合FP16混合精度计算和动态操作融合，我们的方法在单个消费级设备上实现了4K/8K图像的毫秒级推理，同时在多个恢复指标上优于现有最先进（SOTA）模型。
英文摘要：
Considering efficiency, ultra-high-definition (UHD) low-light image restoration is extremely challenging. Existing methods based on Transformer architectures or high-dimensional complex convolutional neural networks often suffer from the "memory wall" bottleneck, failing to achieve millisecond-level inference on edge devices. To address this issue, we propose a novel real-time UHD low-light enhancement network based on geometric feature fusion using Clifford algebra in 2D Euclidean space. First, we construct a four-layer feature pyramid with gradually increasing resolution, which decomposes input images into low-frequency and high-frequency structural components via a Gaussian blur kernel, and adopts a lightweight U-Net based on depthwise separable convolution for dual-branch feature extraction. Second, to resolve structural information loss and artifacts from traditional high-low frequency feature fusion, we introduce spatially aware Clifford algebra, which maps feature tensors to a multivector space (scalars, vectors, bivectors) and uses Clifford similarity to aggregate features while suppressing noise and preserving textures. In the reconstruction stage, the network outputs adaptive Gamma and Gain maps, which perform physically constrained non-linear brightness adjustment via Retinex theory. Integrated with FP16 mixed-precision computation and dynamic operator fusion, our method achieves millisecond-level inference for 4K/8K images on a single consumer-grade device, while outperforming state-of-the-art (SOTA) models on several restoration metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
