Single image super-resolution (SISR) has experienced significant
advancements, primarily driven by deep convolutional networks. Traditional
networks, however, are limited to upscaling images to a fixed scale, leading to
the utilization of implicit neural functions for generating arbitrarily scaled
images. Nevertheless, these methodologies have imposed substantial
computational demands as they involve querying every target pixel to a single
resource-intensive decoder. In this paper, we introduce a novel and efficient
framework, the Mixture of Experts Implicit Super-Resolution (MoEISR), which
enables super-resolution at arbitrary scales with significantly increased
computational efficiency without sacrificing reconstruction quality. MoEISR
dynamically allocates the most suitable decoding expert to each pixel using a
lightweight mapper module, allowing experts with varying capacities to
reconstruct pixels across regions with diverse complexities. Our experiments
demonstrate that MoEISR successfully reduces up to 73% in floating point
operations (FLOPs) while delivering comparable or superior peak signal-to-noise
ratio (PSNR).

单张图像超分辨率（SISR）使用深度卷积网络取得了显著的进展，但传统网络只能将图像放大到固定比例，因此利用隐式神经函数生成任意比例的图像；本文介绍了一种新颖高效的框架 —— 混合专家隐式超分辨率（MoEISR），它在显著提高计算效率的同时，能够以任意比例进行超分辨率重建，而不损失重建质量；MoEISR 利用轻量级映射器模块动态分配最适合的解码专家给每个像素，使具有不同容量的专家能够重建复杂度各异的区域像素；实验证明，MoEISR 能够在缩减高达 73% 的浮点运算（FLOPs）的同时，提供相当或更优的峰值信噪比（PSNR）。