Recent years have witnessed the remarkable success of implicit neural representation methods. The recent work Local Implicit Image Function (LIIF) has achieved satisfactory performance for continuous image representation, where pixel values are inferred from a neural network in a continuous spatial domain. However, the computational cost of such implicit arbitrary-scale super-resolution (SR) methods increases rapidly as the scale factor increases, which makes arbitrary-scale SR time-consuming. In this paper, we propose Dynamic Implicit Image Function (DIIF), which is a fast and efficient method to represent images with arbitrary resolution. Instead of taking an image coordinate and the nearest 2D deep features as inputs to predict its pixel value, we propose a coordinate grouping and slicing strategy, which enables the neural network to perform decoding from coordinate slices to pixel value slices. We further propose a Coarse-to-Fine Multilayer Perceptron (C2F-MLP) to perform decoding with dynamic coordinate slicing, where the number of coordinates in each slice varies as the scale factor varies. With dynamic coordinate slicing, DIIF significantly reduces the computational cost when encountering arbitrary-scale SR. Experimental results demonstrate that DIIF can be integrated with implicit arbitrary-scale SR methods and achieves SOTA SR performance with significantly superior computational efficiency, thereby opening a path for real-time arbitrary-scale image representation. Our code can be found at https://github.com/HeZongyao/DIIF.

翻译：近年来，隐式神经表示方法取得了显著成功。最新提出的局部隐式图像函数（LIIF）通过连续空间域上的神经网络推断像素值，在连续图像表示领域取得了令人满意的性能。然而，这类隐式任意尺度超分辨率（SR）方法的计算成本会随着放大因子的增加而急剧上升，导致任意尺度SR处理耗时严重。本文提出动态隐式图像函数（DIIF），这是一种能够高效表示任意分辨率图像的快速方法。我们摒弃了传统方法中直接输入图像坐标与最近邻二维深度特征以预测像素值的做法，创新性地提出坐标分组与切片策略，使神经网络能够从坐标切片解码得到像素值切片。在此基础上，我们进一步提出粗到细多层感知器（C2F-MLP），通过动态坐标切片机制实现解码，其中每个切片包含的坐标数量随放大因子动态变化。借助动态坐标切片技术，DIIF在处理任意尺度SR时大幅降低了计算成本。实验结果表明，DIIF可无缝集成至隐式任意尺度SR方法，在显著提升计算效率的同时达到最优SR性能，为实时任意尺度图像表示开辟了新路径。我们的代码开源地址为：https://github.com/HeZongyao/DIIF。