Implicit neural representations (INRs) have significantly advanced the field of arbitrary-scale super-resolution (ASSR) of images. Most existing INR-based ASSR networks first extract features from the given low-resolution image using an encoder, and then render the super-resolved result via a multi-layer perceptron decoder. Although these approaches have shown promising results, their performance is constrained by the limited representation ability of discrete latent codes in the encoded features. In this paper, we propose a novel ASSR method named GaussianSR that overcomes this limitation through 2D Gaussian Splatting (2DGS). Unlike traditional methods that treat pixels as discrete points, GaussianSR represents each pixel as a continuous Gaussian field. The encoded features are simultaneously refined and upsampled by rendering the mutually stacked Gaussian fields. As a result, long-range dependencies are established to enhance representation ability. In addition, a classifier is developed to dynamically assign Gaussian kernels to all pixels to further improve flexibility. All components of GaussianSR (i.e., encoder, classifier, Gaussian kernels, and decoder) are jointly learned end-to-end. Experiments demonstrate that GaussianSR achieves superior ASSR performance with fewer parameters than existing methods while enjoying interpretable and content-aware feature aggregations.

翻译：隐式神经表示（INR）显著推动了图像任意尺度超分辨率（ASSR）领域的发展。现有的大多数基于INR的ASSR网络首先使用编码器从给定的低分辨率图像中提取特征，然后通过多层感知机解码器渲染超分辨率结果。尽管这些方法已展现出有前景的结果，但其性能受限于编码特征中离散潜在码的有限表示能力。本文提出了一种名为GaussianSR的新型ASSR方法，该方法通过二维高斯泼溅（2DGS）克服了这一限制。与传统方法将像素视为离散点不同，GaussianSR将每个像素表示为一个连续高斯场。编码特征通过渲染相互堆叠的高斯场同时进行细化和上采样，从而建立长程依赖以增强表示能力。此外，本文还开发了一个分类器，用于动态地将高斯核分配给所有像素，以进一步提升灵活性。GaussianSR的所有组件（即编码器、分类器、高斯核和解码器）均以端到端方式联合学习。实验表明，与现有方法相比，GaussianSR以更少的参数实现了更优的ASSR性能，同时具备可解释且内容感知的特征聚合能力。