动态生成滤波器，提升去雾图像清晰度。
CasDyF-Net: Image Dehazing via Cascaded Dynamic Filters
- 根据特征分布动态生成滤波器，实现分支自适应
- 在RESIDE-Indoor上达到43.21dB的PSNR
- 适合需要高精度去雾的视觉应用
图像去雾旨在通过减少大气散射和吸收效应来恢复图像清晰度与视觉质量。尽管深度学习在此领域取得显著进展，但网络深度限制日益明显。许多方法采用并行分支结构，却常忽视基于输入特征分布的动态分支划分。受动态滤波启发，本文提出级联动态滤波器，依据特征图分布动态生成滤波核，构建多分支网络。为更好处理分支特征，设计残差多尺度块（RMB），融合不同感受野；同时引入基于动态卷积的局部融合方法，整合相邻分支特征。在RESIDE、Haze4K和O-Haze数据集上的实验验证了方法有效性，模型在RESIDE-Indoor数据集上达到43.21dB的PSNR。代码已开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。