The recent work Local Implicit Image Function (LIIF) and subsequent Implicit
Neural Representation (INR) based works have achieved remarkable success in
Arbitrary-Scale Super-Resolution (ASSR) by using MLP to decode Low-Resolution
(LR) features. However, these continuous image representations typically
implement decoding in High-Resolution (HR) High-Dimensional (HD) space, leading
to a quadratic increase in computational cost and seriously hindering the
practical applications of ASSR. To tackle this problem, we propose a novel
Latent Modulated Function (LMF), which decouples the HR-HD decoding process
into shared latent decoding in LR-HD space and independent rendering in HR
Low-Dimensional (LD) space, thereby realizing the first computational optimal
paradigm of continuous image representation. Specifically, LMF utilizes an HD
MLP in latent space to generate latent modulations of each LR feature vector.
This enables a modulated LD MLP in render space to quickly adapt to any input
feature vector and perform rendering at arbitrary resolution. Furthermore, we
leverage the positive correlation between modulation intensity and input image
complexity to design a Controllable Multi-Scale Rendering (CMSR) algorithm,
offering the flexibility to adjust the decoding efficiency based on the
rendering precision. Extensive experiments demonstrate that converting existing
INR-based ASSR methods to LMF can reduce the computational cost by up to 99.9%,
accelerate inference by up to 57 times, and save up to 76% of parameters, while
maintaining competitive performance. The code is available at
this https URL

通过在潜在空间生成潜在调制以快速适应任何输入特征向量并以任意分辨率进行渲染，提出了一种新颖的潜在调制函数（LMF），该函数将高分辨率 - 高维度解码过程解耦为低分辨率 - 高维度空间中的共享潜在解码和低维度空间中的独立渲染，从而实现了连续图像表示的首个计算最优范式。

用于计算优化连续图像表示的潜在调制函数

Latent Modulated Function for Computational Optimal Continuous Image  Representation

Image representation is critical for many visual tasks. Instead of
representing images discretely with 2D arrays of pixels, a recent study, namely
local implicit image function (LIIF), denotes images as a continuous function
where pixel values are expansion by using the corresponding coordinates as
inputs. Due to its continuous nature, LIIF can be adopted for arbitrary-scale
image super-resolution tasks, resulting in a single effective and efficient
model for various up-scaling factors. However, LIIF often suffers from
structural distortions and ringing artifacts around edges, mostly because all
pixels share the same model, thus ignoring the local properties of the image.
In this paper, we propose a novel adaptive local image function (A-LIIF) to
alleviate this problem. Specifically, our A-LIIF consists of two main
components: an encoder and a expansion network. The former captures cross-scale
image features, while the latter models the continuous up-scaling function by a
weighted combination of multiple local implicit image functions. Accordingly,
our A-LIIF can reconstruct the high-frequency textures and structures more
accurately. Experiments on multiple benchmark datasets verify the effectiveness
of our method. Our codes are available at
https://github.com/LeeHW-THU/A-LIIF.

本研究提出了一种新的自适应局部图像函数（A-LIIF）模型，旨在通过采用编码器和扩展网络，建模多个局部隐式图像函数的加权组合，从而缓解局部隐式图像函数（LIIF）模型在边缘周围出现结构失真和环状伪影等问题，实现图像超分辨率的高精度重建。