GAMA-IR：用于快速图像复原的全局加性多维平均方法
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration
AI总结：
本文提出GAMA-IR，一种基于全局加性多维平均模块的浅层图像复原网络，在保持最先进性能的同时显著降低延迟和内存消耗，并在SIDD去噪上以0.11dB优势超越现有最佳结果且速度提升2至10倍。
摘要：
基于深度学习的方法已在去噪和去模糊等多种图像复原任务中展现出显著成效。当前最先进的网络相对较深，并采用（各种变体的）自注意力机制。这些网络比浅层卷积网络慢得多，而浅层卷积网络性能较差。本文提出了一种既快速又能产生优异图像质量的图像复原网络。该网络旨在在标准GPU上执行时最小化延迟和内存消耗，同时保持最先进的性能。该网络是一个简单的浅层网络，包含一个实现全局加性多维平均运算的高效模块。该模块能够捕获全局信息，并在浅层网络中以最小的计算开销实现大感受野。通过在多种任务上的大量实验和评估，我们证明该网络以更低的延迟取得了与现有最先进图像复原网络相当甚至更优的结果。例如，我们在真实世界SIDD去噪任务上以0.11dB的优势超越了最先进结果，同时速度提高了2到10倍。
英文摘要：
Deep learning-based methods have shown remarkable success for various image restoration tasks such as denoising and deblurring. The current state-of-the-art networks are relatively deep and utilize (variants of) self attention mechanisms. Those networks are significantly slower than shallow convolutional networks, which however perform worse. In this paper, we introduce an image restoration network that is both fast and yields excellent image quality. The network is designed to minimize the latency and memory consumption when executed on a standard GPU, while maintaining state-of-the-art performance. The network is a simple shallow network with an efficient block that implements global additive multidimensional averaging operations. This block can capture global information and enable a large receptive field even when used in shallow networks with minimal computational overhead. Through extensive experiments and evaluations on diverse tasks, we demonstrate that our network achieves comparable or even superior results to existing state-of-the-art image restoration networks with less latency. For instance, we exceed the state-of-the-art result on real-world SIDD denoising by 0.11dB, while being 2 to 10 times faster.
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。
