Image dehazing aims to restore image clarity and visual quality by reducing atmospheric scattering and absorption effects. While deep learning has made significant strides in this area, more and more methods are constrained by network depth. Consequently, lots of approaches have adopted parallel branching strategies. however, they often prioritize aspects such as resolution, receptive field, or frequency domain segmentation without dynamically partitioning branches based on the distribution of input features. Inspired by dynamic filtering, we propose using cascaded dynamic filters to create a multi-branch network by dynamically generating filter kernels based on feature map distribution. To better handle branch features, we propose a residual multiscale block (RMB), combining different receptive fields. Furthermore, we also introduce a dynamic convolution-based local fusion method to merge features from adjacent branches. Experiments on RESIDE, Haze4K, and O-Haze datasets validate our method's effectiveness, with our model achieving a PSNR of 43.21dB on the RESIDE-Indoor dataset. The code is available at https://github.com/dauing/CasDyF-Net.

本研究针对传统图像去雾方法在网络深度受限的问题，提出了一种创新的级联动态过滤器网络。该方法通过动态生成过滤器内核，结合残差多尺度区块，有效提升特征处理能力，实验表明我们的模型在RESIDE-Indoor数据集上获得了43.21dB的PSNR，显示出其显著的图像恢复效果。

CasDyF-Net：通过级联动态过滤器进行图像去雾