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。局部隐式图像函数（LIIF）通过连续空间域中的神经网络推断像素值，在连续图像表示方面实现了令人满意的性能。然而，此类隐式任意尺度超分辨率方法的计算成本随尺度因子增加而快速增长，导致任意尺度超分辨率耗时较长。本文提出动态隐式图像函数（DIIF），这是一种快速高效的任意分辨率图像表示方法。我们不再将图像坐标和最近的二维深度特征作为输入来预测像素值，而是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为像素值切片。进一步提出粗到细多层感知器，通过动态坐标切片执行解码，其中每个切片中的坐标数量随尺度因子变化。通过动态坐标切片，DIIF在处理任意尺度超分辨率时显著降低了计算成本。实验结果表明，DIIF可集成至隐式任意尺度超分辨率方法中，以显著优越的计算效率实现最先进的超分辨率性能，从而为实时任意尺度图像表示开辟新路径。我们的代码可在 https://github.com/HeZongyao/DIIF 获取。