Abstract: To address the problems of complex rain streak distributions, confusion between rain streaks and background textures, and redundant parameters in deraining networks, this paper constructs a lightweight adversarial deraining network based on feature mapping. Existing deep learning-based deraining methods have achieved good performance on synthetic rainy images. However, in heavy rain, oblique rain streaks, local occlusion, and regions where rain streaks overlap with background edges, they may still suffer from residual rain streaks, blurred edges, and insufficient detail recovery. Meanwhile, some deraining models rely on complex feature extraction structures, leading to large parameter numbers and high computational costs, which makes them less suitable for resource-constrained scenarios such as vehicle-mounted terminals, roadside perception devices, and edge computing platforms. Therefore, this paper improves an adversarial deraining network from two aspects: input feature representation enhancement and model lightweighting.In terms of network structure, an adversarial generation framework is adopted as the basic model. The generator maps rainy images to derained images, while the discriminator distinguishes generated images from real rain-free images. During training, reconstruction loss and adversarial loss are jointly used to balance pixel-level consistency and visual naturalness. Considering that rain streaks are usually thin, directional, unevenly distributed, and rich in high-frequency components, Fourier feature mapping is introduced at the input stage. Two-dimensional spatial coordinates are expanded into multi-frequency positional encodings and concatenated with shallow image features to form enhanced input features. This provides the generator with richer spatial-frequency information, improves its ability to perceive rain locations, edge structures, and local texture regions, and reduces restoration errors caused by the confusion between rain streaks and background details.For model lightweighting, a frequency-domain channel pruning strategy is designed. The intermediate features of the generator are first transformed into the frequency domain by Fourier transform, and the frequency-domain energy distribution of each channel is then calculated. Channel importance is evaluated according to the response to high-frequency details and structural information. Channels that contribute more to rain modeling and detail recovery are retained, while low-contribution redundant channels are pruned to reduce the number of parameters. This pruning process is mainly performed after training, so it does not introduce additional computational burden during inference.In the experiments, Rain14000 is used as the training set, and Rain100H, Rain100L, Rain12, and the real rainy dataset SPA-Data are used for testing. Quantitative results show that the proposed method achieves PSNR/SSIM values of 30.96 dB/0.900, 37.62 dB/0.975, and 35.41 dB/0.959 on Rain100H, Rain100L, and Rain12, respectively. Compared with the Base model, the proposed method improves PSNR by 0.76 dB, 0.82 dB, and 0.61 dB on the three datasets, respectively, indicating stable restoration performance under different rain conditions.Ablation experiments show that Fourier feature mapping improves PSNR and SSIM on Rain100H and SPA-Data, demonstrating its ability to enhance the modeling of rain spatial distribution and high-frequency details. When only frequency-domain channel pruning is applied, the number of parameters is reduced, while the restoration metrics decrease slightly. When Fourier feature mapping and frequency-domain pruning are combined, the model achieves 30.96 dB/0.900 on Rain100H and 29.42 dB/0.925 on SPA-Data, with the parameter number reduced to 0.69M. This indicates that the two components are complementary in feature enhancement and model compression. Qualitative results show that the proposed method can reduce residual rain streaks and better preserve human edges, umbrella regions, and complex background textures. Overall, the experimental results demonstrate that the proposed method improves deraining quality while controlling model size, providing a lightweight solution for image restoration in complex rainy scenes.

摘要： 针对雨天图像中雨纹分布复杂、背景纹理易混淆以及去雨网络参数冗余等问题，本文构建了一种基于特征映射的对抗去雨轻量化网络。现有深度学习去雨方法在合成雨图像上已取得较好效果，但在强雨、斜向雨纹、局部遮挡以及雨纹与背景边缘交叠区域，仍容易出现雨纹残留、边缘模糊和细节恢复不足等现象。同时，部分去雨模型依赖复杂特征提取结构，参数量和计算开销较大，不利于在车载终端、路侧感知设备和边缘计算平台等资源受限场景中部署。为此，本文从输入特征表达增强和模型轻量化两个方面对对抗生成去雨网络进行改进。在网络结构上，本文以对抗生成框架作为基础模型。生成器用于完成含雨图像到去雨图像的映射，判别器用于区分生成图像和真实无雨图像。训练过程中，网络联合采用重建损失和对抗损失，使生成结果在像素一致性和视觉自然性之间取得平衡。考虑到雨纹通常具有细长、方向明显、局部分布不均和高频特征突出的特点，本文在输入阶段引入傅里叶特征映射，将二维空间坐标扩展为多频率位置编码，并与浅层图像特征拼接，形成增强输入特征。该方式能够为生成器提供更丰富的空间频率信息，提高网络对雨纹位置、边缘结构和局部纹理区域的感知能力，从而减轻雨纹与背景细节混淆带来的恢复误差。在模型轻量化方面，本文设计了基于频域信息的通道剪枝策略。该策略以生成器中间层特征为分析对象，先对各通道特征进行傅里叶变换，再统计其频域能量分布，并根据通道对高频细节和结构信息的响应情况评估重要性。随后，保留对雨纹建模和细节恢复贡献较大的通道，裁剪低贡献冗余通道，以减少模型参数量。该剪枝过程主要在训练完成后的轻量化阶段进行，因此不会额外增加推理阶段的计算负担。实验中，本文采用 Rain14000 数据集进行训练，并在 Rain100H、Rain100L、Rain12 和真实雨天数据集 SPA-Data 上进行测试。定量结果表明，本文方法在 Rain100H、Rain100L 和 Rain12 数据集上的 PSNR/SSIM 分别达到 30.96 dB/0.900、37.62 dB/0.975 和 35.41 dB/0.959。与 Base 模型相比，本文方法在三个数据集上的 PSNR 分别提升 0.76 dB、0.82 dB 和 0.61 dB，说明该方法在不同雨型场景下具有较稳定的恢复能力。消融实验表明，引入傅里叶特征映射后，模型在 Rain100H 和 SPA-Data 上的 PSNR 与 SSIM 均有所提升，说明该模块能够增强网络对雨纹空间分布和高频细节区域的建模能力。仅采用频域通道剪枝时，模型参数量下降，但恢复指标略有降低；当傅里叶特征映射与频域剪枝结合后，模型在 Rain100H 和 SPA-Data 上分别取得 30.96 dB/0.900 和 29.42 dB/0.925 的结果，参数量降低至 0.69M，说明二者在特征增强和模型压缩之间具有互补作用。定性结果显示，本文方法能够减少残余雨纹，并较好保持人物边缘、雨伞区域和复杂背景纹理。综合实验结果可知，本文方法在提升去雨质量的同时能够控制模型规模，为复杂雨天图像恢复提供了一种较轻量的实现思路。