DiffFNO: Diffusion Fourier Neural Operator

• URL: http://arxiv.org/abs/2411.09911v1
• Date: Fri, 15 Nov 2024 03:14:11 GMT
• Title: DiffFNO: Diffusion Fourier Neural Operator
• Authors: Xiaoyi Liu, Hao Tang,
• Abstract summary: We introduce DiffFNO, a novel diffusion framework for arbitrary-scale super-resolution strengthened by a Weighted Fourier Neural Operator (WFNO) We show that DiffFNO achieves state-of-the-art (SOTA) results, outperforming existing methods across various scaling factors by a margin of 2 to 4 dB in PSNR. Our approach sets a new standard in super-resolution, delivering both superior accuracy and computational efficiency.
• Score: 8.895165270489167
• License:
• Abstract: We introduce DiffFNO, a novel diffusion framework for arbitrary-scale super-resolution strengthened by a Weighted Fourier Neural Operator (WFNO). Mode Re-balancing in WFNO effectively captures critical frequency components, significantly improving the reconstruction of high-frequency image details that are crucial for super-resolution tasks. Gated Fusion Mechanism (GFM) adaptively complements WFNO's spectral features with spatial features from an Attention-based Neural Operator (AttnNO). This enhances the network's capability to capture both global structures and local details. Adaptive Time-Step (ATS) ODE solver, a deterministic sampling strategy, accelerates inference without sacrificing output quality by dynamically adjusting integration step sizes ATS. Extensive experiments demonstrate that DiffFNO achieves state-of-the-art (SOTA) results, outperforming existing methods across various scaling factors by a margin of 2 to 4 dB in PSNR, including those beyond the training distribution. It also achieves this at lower inference time. Our approach sets a new standard in super-resolution, delivering both superior accuracy and computational efficiency.

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