Abstract
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.
Abstract (translated)
近年来，图像去噪技术的主要关注点是在频域和时域使用傅里叶变换（FT）特性。然而，由于FT对静止信号的依赖以及它缺乏提取空间-频域特性的能力，这些算法的性能受到限制。在本文中，我们提出了一个基于分式傅里叶变换（FRFT）的新颖方法，一种同时利用空间和频域成分的统一空间-频域表示，使得它非常适合处理非静止信号，如图像。具体来说，我们引入了一个基于新分数傅里叶变换（F2TB）的多分支编码器-解码器变换器，将经典的分数傅里叶基于维纳解卷（F2WD）与基于新分数频关注变换（F2TB）相结合。我们设计了一个基于分数频关注的自注意（F2SA），用于根据重要频率分量进行元素级乘积注意，以及一个基于频率分割多路复用（FM-FFN）的新傅里叶变换网络，用于分别对高和低频率特征进行精炼，实现高效的潜在图像去噪。对于运动去噪和失焦去噪案例的实验结果表明，我们提出的方法在性能上优于其他最先进的（SOTA）方法。
URL
https://arxiv.org/abs/2409.02056
