用分数阶傅里叶变换提升图像去模糊效果
F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring
- 引入分数阶傅里叶变换融合时空频特性，适配非平稳图像信号
- 在Motion、Defocus数据集上优于现有最先进方法
- 适合图像去模糊与频域建模研究者参考
近期图像去模糊技术主要依赖傅里叶变换在频域与空域联合操作，但受限于傅里叶变换对平稳信号的依赖及提取空间-频率特性的能力不足。本文提出一种基于分数阶傅里叶变换（FRFT）的新方法，其作为统一的时空频表示，可同时利用空间与频率成分，特别适用于处理如图像般的非平稳信号。具体地，我们设计了分数阶傅里叶变换器（F2former），结合经典分数阶傅里叶维纳反卷积（F2WD）以及基于新型分数频域感知变换器块（F2TB）的多分支编码器-解码器结构。F2TB包含分数频域感知自注意力（F2SA），通过重要频段估计逐元素乘积注意力；以及基于频分复用（FM-FFN）的新型前馈网络，分别优化高低频特征以实现高效清晰图像恢复。实验结果表明，在运动去模糊与散焦去模糊任务中，所提方法性能均优于其他现有最先进（SOTA）方法。
原文摘要 · Abstract (English)
Recent progress in image deblurring techniques focuses mainly on operating in both frequency and spatial domains using the Fourier transform (FT) properties. However, their performance is limited due to the dependency of FT on stationary signals and its lack of capability to extract spatial-frequency properties. In this paper, we propose a novel approach based on the Fractional Fourier Transform (FRFT), a unified spatial-frequency representation leveraging both spatial and frequency components simultaneously, making it ideal for processing non-stationary signals like images. Specifically, we introduce a Fractional Fourier Transformer (F2former), where we combine the classical fractional Fourier based Wiener deconvolution (F2WD) as well as a multi-branch encoder-decoder transformer based on a new fractional frequency aware transformer block (F2TB). We design F2TB consisting of a fractional frequency aware self-attention (F2SA) to estimate element-wise product attention based on important frequency components and a novel feed-forward network based on frequency division multiplexing (FM-FFN) to refine high and low frequency features separately for efficient latent clear image restoration. Experimental results for the cases of both motion deblurring as well as defocus deblurring show that the performance of our proposed method is superior to other state-of-the-art (SOTA) approaches.
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