标题： 任意尺度超分辨率的广义高效二维高斯泼溅 显示英文标题

标题： Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-Resolution

摘要： 隐式神经表示（INR）已被成功用于任意尺度的超分辨率（ASR）。 然而，基于INR的模型需要多次查询多层感知机模块，并在每次查询中渲染一个像素，导致表示能力和计算效率不足。 最近，高斯点云（GS）在3D任务中的视觉质量和渲染速度方面表现出优于INR的优势，这促使我们探索GS是否可用于ASR任务。 然而，直接将GS应用于ASR极具挑战性，因为原始GS是一种通过过拟合每个单个场景的优化方法，而在ASR中，我们旨在学习一个能够泛化到不同图像和缩放因子的单一模型。 我们通过开发两种新技术克服了这些挑战。 首先，为了使GS适用于ASR，我们精心设计了一个架构，以前馈方式预测输入低分辨率图像对应的图像条件高斯分布。 每个高斯分布可以拟合复杂纹理区域的形状和方向，表现出强大的表示能力。 其次，我们实现了一种高效的可微分的基于2D GPU/CUDA的尺度感知光栅化，通过从预测的连续高斯分布中采样离散RGB值来渲染超分辨率图像。 通过端到端训练，我们优化的网络，即GSASR，可以对任何图像和未见过的缩放因子进行ASR。 大量实验验证了我们所提出方法的有效性。 显示英文摘要

摘要： Implicit Neural Representation (INR) has been successfully employed for Arbitrary-scale Super-Resolution (ASR). However, INR-based models need to query the multi-layer perceptron module numerous times and render a pixel in each query, resulting in insufficient representation capability and computational efficiency. Recently, Gaussian Splatting (GS) has shown its advantages over INR in both visual quality and rendering speed in 3D tasks, which motivates us to explore whether GS can be employed for the ASR task. However, directly applying GS to ASR is exceptionally challenging because the original GS is an optimization-based method through overfitting each single scene, while in ASR we aim to learn a single model that can generalize to different images and scaling factors. We overcome these challenges by developing two novel techniques. Firstly, to generalize GS for ASR, we elaborately design an architecture to predict the corresponding image-conditioned Gaussians of the input low-resolution image in a feed-forward manner. Each Gaussian can fit the shape and direction of an area of complex textures, showing powerful representation capability. Secondly, we implement an efficient differentiable 2D GPU/CUDA-based scale-aware rasterization to render super-resolved images by sampling discrete RGB values from the predicted continuous Gaussians. Via end-to-end training, our optimized network, namely GSASR, can perform ASR for any image and unseen scaling factors. Extensive experiments validate the effectiveness of our proposed method.