用于单图像去雨的Gabor引导Transformer
Gabor-guided transformer for single image deraining
AI总结：
针对现有单图像去雨方法全局信息捕获不足或易损失高频细节的问题，提出Gabor引导的Transformer（Gabformer），将Gabor滤波信息融入查询向量以增强纹理关注和抗噪性，在基准测试中性能超越现有最优方法。
摘要：
为应对恶劣天气条件对视觉任务造成的挑战，图像去雨研究已受到广泛关注。尽管卷积神经网络（CNNs）应用广泛，但其在捕获全局信息方面的局限性可能导致去雨效果不佳。基于Transformer且带有自注意力机制的方法虽有改进，但往往会破坏对图像保真度至关重要的高频细节。为解决这一问题，我们提出用于单图像去雨的Gabor引导Transformer（Gabformer）。该方法通过将Gabor滤波器处理后的信息融入查询向量，增强了对局部纹理特征的关注，同时凭借滤波器的特性提升了模型对噪声的鲁棒性。在多个基准数据集上的大量实验表明，我们的方法性能优于当前最先进的方法。
英文摘要：
Image deraining have have gained a great deal of attention in order to address the challenges posed by the effects of harsh weather conditions on visual tasks. While convolutional neural networks (CNNs) are popular, their limitations in capturing global information may result in ineffective rain removal. Transformer-based methods with self-attention mechanisms have improved, but they tend to distort high-frequency details that are crucial for image fidelity. To solve this problem, we propose the Gabor-guided tranformer (Gabformer) for single image deraining. The focus on local texture features is enhanced by incorporating the information processed by the Gabor filter into the query vector, which also improves the robustness of the model to noise due to the properties of the filter. Extensive experiments on the benchmarks demonstrate that our method outperforms state-of-the-art approaches.
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