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 this https URL

DIIF 是一种动态隐式图像函数，其使用坐标分组和切片策略从坐标切片到像素值切片进行解码，以显着降低计算成本，并且可以与超分辨率方法集成以实现令人满意的性能。