Fourier-Guided Attention Upsampling for Image Super-Resolution

• URL: http://arxiv.org/abs/2508.10616v2
• Date: Sat, 23 Aug 2025 06:41:59 GMT
• Title: Fourier-Guided Attention Upsampling for Image Super-Resolution
• Authors: Daejune Choi, Youchan No, Jinhyung Lee, Duksu Kim,
• Abstract summary: Frequency-Guided Attention (FGA) is a lightweight upsampling module for single image super-resolution.<n>Trials show average PSNR gains of 0.120.14 dB and improved frequency-domain consistency by up to 29%.
• Score: 0.13999481573773068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Frequency-Guided Attention (FGA), a lightweight upsampling module for single image super-resolution. Conventional upsamplers, such as Sub-Pixel Convolution, are efficient but frequently fail to reconstruct high-frequency details and introduce aliasing artifacts. FGA addresses these issues by integrating (1) a Fourier feature-based Multi-Layer Perceptron (MLP) for positional frequency encoding, (2) a cross-resolution Correlation Attention Layer for adaptive spatial alignment, and (3) a frequency-domain L1 loss for spectral fidelity supervision. Adding merely 0.3M parameters, FGA consistently enhances performance across five diverse super-resolution backbones in both lightweight and full-capacity scenarios. Experimental results demonstrate average PSNR gains of 0.12~0.14 dB and improved frequency-domain consistency by up to 29%, particularly evident on texture-rich datasets. Visual and spectral evaluations confirm FGA's effectiveness in reducing aliasing and preserving fine details, establishing it as a practical, scalable alternative to traditional upsampling methods.

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