Dilated-convolutional feature modulation network for efficient image super-resolution

Lijun Wu

doi:10.1007/s11554-025-01663-5

Dilated-convolutional feature modulation network for efficient image super-resolution

2025-03-21

0

PRE

AI

DOI：10.1007/s11554-025-01663-5

Original

Original request for help

Save

Abstract

En 中文

In the field of image super-resolution (SR), deep learning-based models have achieved remarkable success. However, these models often face compatibility issues with low-power devices due to their computational and memory constraints. To address this challenge, numerous lightweight and efficient models have been proposed. While these models typically employ smaller convolutional kernels and shallower architectures to reduce parameter counts and computational complexity, they often neglect the importance of capturing global receptive fields. In this paper, we propose a simple yet effective deep network, termed the dilated-convolutional feature modulation network (DCFMN), to tackle these limitations. Specifically, we introduce a dilated separable modulation unit (DSMU) to aggregate spatial information from diverse large receptive fields. To complement the DSMU, which processes features from a long-range perspective, we further design a local feature enhancement module (LFEM) to extract local contextual information for effective channel fusion. Additionally, by leveraging reparameterization techniques, we ensure that the model incurs no additional computational overhead during inference. Extensive experimental results demonstrate that our DCFMN achieves competitive performance among existing efficient SR methods, while maintaining a compact model size and low computational complexity.

Keywords:

Image super-resolution

Deep learning

Lightweight

Reparameterization

Dilated-convolutional feature modulation network for efficient image super-resolution

Abstract

Journal

Organization