Multi-Scale Feature Adaptive Modulation for Single Image Super-Resolution

SHEN Wei-lu; LIU Jie; TANG Jie; WU Gang-shan

doi:10.12263/DZXB.20250032

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Multi-Scale Feature Adaptive Modulation for Single Image Super-Resolution

PAPERS | 更新时间：2025-12-10

- Multi-Scale Feature Adaptive Modulation for Single Image Super-Resolution
- ACTA ELECTRONICA SINICA Vol. 53, Issue 7, Pages: 2324-2341(2025)
- 作者机构：
  
  南京大学计算机学院，江苏南京 210023
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62402211);Natural Science Foundation of Jiangsu Province(BK20241248)
- DOI：10.12263/DZXB.20250032
  CLC： TP391.41;
- Received：08 January 2025，
  
  Revised：2025-07-15，
  
  Published：25 July 2025
- 稿件说明：
移动端阅览
沈伟露, 刘杰, 唐杰, 等. 基于多尺度特征自适应调制的单图像超分辨率网络[J]. 电子学报, 2025, 53(07): 2324-2341.

SHEN Wei-lu, LIU Jie, TANG Jie, et al. Multi-Scale Feature Adaptive Modulation for Single Image Super-Resolution[J]. Acta Electronica Sinica, 2025, 53(07): 2324-2341.
沈伟露, 刘杰, 唐杰, 等. 基于多尺度特征自适应调制的单图像超分辨率网络[J]. 电子学报, 2025, 53(07): 2324-2341. DOI：10.12263/DZXB.20250032

SHEN Wei-lu, LIU Jie, TANG Jie, et al. Multi-Scale Feature Adaptive Modulation for Single Image Super-Resolution[J]. Acta Electronica Sinica, 2025, 53(07): 2324-2341. DOI：10.12263/DZXB.20250032

摘要

基于Transformer的图像恢复方法在单图像超分辨率（Single Image Super-Resolution，SISR）任务中展现了卓越的性能，这得益于其自注意力（Self-Attention，SA）机制能够有效捕捉非局部信息，从而实现更高质量的高分辨率（High Resolution，HR）图像重建.然而，SA机制中的矩阵乘法操作需要消耗大量计算资源，这使得基于Transformer的模型通常难以适配计算能力和内存受限的低功耗设备.此外，SA机制的低通特性限制了其捕获高频局部细节的能力，从而导致平滑的重建结果.为了解决以上问题，本文提出了一种基于多尺度特征自适应调制的单图像超分辨率网络（Multi-scale Feature Adaptive Modulation Network，MFAMNet），其核心是多尺度特征自适应调制（Multi-scale Feature Adaptive Modulation，MFAM）模块，该模块通过下采样操作获取不同尺度的低频内容，计算输入特征的全局方差来调制处理后的低频特征，然后使用调制后的特征自适应地聚合输入特征，从而实现对非局部信息的高效建模.在聚合输入特征之后，本文引入通道注意力机制，从通道维度对融合特征进行细化处理，以增强所有通道间公共信息的提取能力，同时实现跨通道权重的动态重分配.此外，由于MFAM从远程角度处理输入特征，因此需要补充局部上下文信息.为此，本文还设计了空间增强模块（Spatial Enhancement Module，SEM），作为复杂自注意机制的有效替代方案，以显著提高空间局部聚合能力，在空间和通道维度上进一步细化从MFAM输出的特征.大量实验表明：所提出的MFAMNet，在公共基准数据集上实现了重建性能和计算效率之间的更佳权衡.特别是在4倍超分辨率（Super-Resolution，SR）下，与最新的自调制特征聚合网络（Self-Modulation Feature Aggregation Network，SMFANet）相比，MFAMNet在五个公共测试集上的平均性能提高了0.15 dB，而模型复杂性，如每秒浮点运算次数（FLoating-point Operations Per second，FLOPs），与前者几乎相同.

Abstract

Transformer-based image restoration methods have demonstrated remarkable performance in single image super-resolution tasks

owing to their self-attention (SA) mechanism

which effectively captures non-local information

thereby achieving higher-quality high-resolution image reconstruction. However

the matrix multiplication operations in the self-attention mechanism consume substantial computational resources

making Transformer-based models generally challenging to deploy on low-power devices with limited computational capabilities and memory. Additionally

the low-pass characteristics of the SA mechanism restrict its ability to capture high-frequency local details

leading to overly smooth reconstruction results. To address these issues

we propose a multi-scale feature adaptive modulation network (MFAMNet) for single image super-resolution

whose core is the multi-scale feature adaptive modulation (MFAM) module. This module obtains low-frequency content at different scales through downsampling operations

computes the global variance of the input features to modulate the processed low-frequency features

and then adaptively aggregates the input features using the modulated features

thereby achieving efficient modeling of non-local information. After aggregating the input features

we introduce a channel attention mechanism to refine the fused features from the channel dimension

enhancing the extraction of shared information across all channels while dynamically reallocating cross-channel weights. Furthermore

since MFAM processes input features from a long-range perspective

it is necessary to supplement local contextual information. To this end

we also design a spatial enhancement module (SEM) as an effective alternative to complex self-attention mechanisms

significantly improving spatial local aggregation capabilities and further refining the features output from MFAM in both spatial and channel dimensions. Extensive experiments demonstrate that the proposed MFAMNet achieves a better trade-off between reconstruction performance and computational efficiency on public benchmark datasets. Notably

in 4× super-resolution

self-modulation feature aggregation network (MFAMNet) improves the average performance by 0.15 dB compared to the state-of-the-art self-modulation feature aggregation network (SMFANet) on five public test sets

while maintaining nearly the same model complexity

e.g.

floating-point operations per second (FLOPs).

关键词

Keywords

references

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