Residual block-based complexity-aware neural ODE fusion for image super-resolution
TL;DR
This paper proposes Adaptive ODE-ResNet (Adaptive ODE-ResNet), which reconstructs the residual block into a continuous ODE-driven process to realize flexible and accurate feature evolution and provides an accurate, efficient and scalable continuous-time modeling scheme for high-resolution image reconstruction.
Abstract
Image super-resolution aims to reconstruct high-resolution images from low-resolution inputs and is a core reconstruction task in computer vision. Traditional discrete residual network-based methods realize feature transformation by stacking a fixed number of layers, but are limited by the discrete depth, making it difficult to capture continuous image degradation and reconstruction dynamics. In recent years, Neural Ordinary Differential Equations (ODEs) have received widespread attention for their continuous depth modeling capability, but face challenges such as high computational cost and large feature dimensions in super-resolution tasks. In this paper, we propose Adaptive ODE-ResNet (Adaptive ODE-ResNet), which reconstructs the residual block into a continuous ODE-driven process to realize flexible and accurate feature evolution. The core innovations include two points: first, the space complexity-aware solver (SCAS), which dynamically adjusts the ODE integration step according to the local texture complexity, reducing the computational overhead in flat regions and improving the reconstruction accuracy in texture-rich regions, and bringing a + 0.55 dB gain; and second, the low-rank ODE projection (LROP), which compresses the high-dimensional ODE features through low-rank decomposition, with an accuracy loss of less than 0.05 dB under fourfold compression. is lower than 0.05 dB. In addition, an independent ODE function design enhanced by CBAM channel-space dual-attention mechanism is proposed to realize fine feature evolution with 640-channel feature space together with 24-step RK4 integration. The method achieves 29.27 dB PSNR and 0.8107 SSIM on the DIV2K ×4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times 4$$\end{document} benchmark; on the × 2 task, it achieves 38.30 dB PSNR on Set5, which exceeds the ODE-SR method (38.21 dB) and approaches the EDSR level, verifying the validity and competitiveness of the adaptive ODE framework with multiple magnification factors. The framework provides an accurate, efficient and scalable continuous-time modeling scheme for high-resolution image reconstruction.