Abstract
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.
Abstract (translated)
最近基于MLP的局部隐式图像函数（LIIF）和后续的隐式神经表示（INR）在任意尺度超分辨率（ASSR）方面的研究取得了显著的成功，通过使用MLP解码低分辨率（LR）特征。然而，这些连续的图像表示通常在高分辨率（HR）和高维度（HD）空间中执行解码，导致计算成本增加，严重阻碍了ASSR的实际应用。为了解决这个问题，我们提出了一个新颖的潜在模块函数（LMF），它将高分辨率（HR）和高维度（HD）解码过程在LR-HD空间中进行共享隐式解码，在HR低维度（LD）空间中实现独立渲染，从而实现了连续图像表示的第一个计算最优范式。具体来说，LMF利用高维度（HD）的MLP在隐空间中生成每个LR特征向量的隐式模度。这使得在渲染空间中，模度强度与输入特征向量呈正相关，从而实现对输入图像复杂度的自适应调整。此外，我们利用模度强度与输入图像复杂度之间的正相关性，设计了一个可控制多尺度渲染（CMSR）算法，根据渲染精度调整解码效率。大量实验证明，将现有的INR为基础的ASSR方法转换为LMF可以降低计算成本至99.9%，加速推理至57倍，并节省约76%的参数，同时保持竞争力的性能。代码可在此处访问：https://url.com/
URL
https://arxiv.org/abs/2404.16451
