WFDENet: Wavelet-based frequency decomposition and enhancement network for diabetic retinopathy lesion segmentation

The acquisition of precise semantic and detailed information is indispensable for high-accuracy diabetic retinopathy lesion segmentation (DRLS). To achieve this, noticing that high- and low-level encoder features respectively contain rich semantics and details, most existing DRLS methods focus on the design of delicate multi-level feature refinement and fusion manners. However, they ignore the exploration of intrinsic low- and high-frequency information of multi-level features, which can also describe the semantics and details. To fill this gap, we propose a Wavelet-based Frequency Decomposition and Enhancement Network (WFDENet), which simultaneously refines semantic and detailed representations by enhancing the low- and high-frequency components of the multi-level encoder features. Specifically, the low- and high-frequency components, which are acquired via discrete wavelet transform (DWT), are boosted by a low-frequency booster (LFB) and a high-frequency booster (HFB), respectively. High-frequency components contain abundant details but also more noise. To suppress the noise and strengthen critical features, in HFB, we devise a complex convolutional frequency attention module (CCFAM), which utilizes complex convolutions to generate dynamic complex-valued channel and spatial attention to improve the Fourier spectrum of high-frequency components. Moreover, considering the importance of multi-scale information, we aggregate the multi-scale frequency features to enrich the frequency components in both LFB and HFB. Experimental results on IDRiD and DDR datasets show that our WFDENet outperforms state-of-the-art methods. The source code is available at https://github.com/xuanli01/WFDENet.