标题： 旋转等变的任意尺度图像超分辨率 显示英文标题

标题： Rotation Equivariant Arbitrary-scale Image Super-Resolution

摘要： 任意尺度图像超分辨率（ASISR），是计算机视觉中的一个近期热门话题，旨在从低分辨率输入图像中实现任意尺度的高分辨率恢复。 该任务通过将图像表示为连续隐函数来实现，涉及两个基本模块，基于深度网络的编码器和隐式神经表示（INR）模块。 尽管取得了显著进展，这种高度不适定的设置中存在一个关键挑战，即许多常见的几何模式，如重复纹理、边缘或形状，在低分辨率图像中严重扭曲和变形，自然导致其高分辨率恢复中出现意外的伪影。 因此，将旋转等变性嵌入到ASISR网络中是必要的，因为已广泛证明这种增强能够使恢复过程忠实保持输入图像中几何模式的原始方向和结构完整性。 受此启发，我们在本研究中努力构建一种具有旋转等变性的ASISR方法。 具体而言，我们精心重新设计了INR和编码器模块的基本架构，除了传统ASISR网络的能力外，还融入了内在的旋转等变能力。 通过这种改进，ASISR网络可以首次在从输入到输出的端到端旋转等变性得到保持的情况下进行实现。 我们还提供了坚实的理论分析来评估其内在等变误差，证明了嵌入这种等变结构的固有性质。 所提出方法的优势通过在模拟和真实数据集上进行的实验得到了证实。 我们还验证了所提出的框架可以以即插即用的方式轻松集成到当前的ASISR方法中，以进一步提升其性能。 显示英文摘要

摘要： The arbitrary-scale image super-resolution (ASISR), a recent popular topic in computer vision, aims to achieve arbitrary-scale high-resolution recoveries from a low-resolution input image. This task is realized by representing the image as a continuous implicit function through two fundamental modules, a deep-network-based encoder and an implicit neural representation (INR) module. Despite achieving notable progress, a crucial challenge of such a highly ill-posed setting is that many common geometric patterns, such as repetitive textures, edges, or shapes, are seriously warped and deformed in the low-resolution images, naturally leading to unexpected artifacts appearing in their high-resolution recoveries. Embedding rotation equivariance into the ASISR network is thus necessary, as it has been widely demonstrated that this enhancement enables the recovery to faithfully maintain the original orientations and structural integrity of geometric patterns underlying the input image. Motivated by this, we make efforts to construct a rotation equivariant ASISR method in this study. Specifically, we elaborately redesign the basic architectures of INR and encoder modules, incorporating intrinsic rotation equivariance capabilities beyond those of conventional ASISR networks. Through such amelioration, the ASISR network can, for the first time, be implemented with end-to-end rotational equivariance maintained from input to output. We also provide a solid theoretical analysis to evaluate its intrinsic equivariance error, demonstrating its inherent nature of embedding such an equivariance structure. The superiority of the proposed method is substantiated by experiments conducted on both simulated and real datasets. We also validate that the proposed framework can be readily integrated into current ASISR methods in a plug \& play manner to further enhance their performance.