Existing single image reflection removal (SIRR) methods using deep learning
tend to miss key low-frequency (LF) and high-frequency (HF) differences in
images, affecting their effectiveness in removing reflections. To address this
problem, this paper proposes a novel prompt-guided reflection removal
(PromptRR) framework that uses frequency information as new visual prompts for
better reflection performance. Specifically, the proposed framework decouples
the reflection removal process into the prompt generation and subsequent
prompt-guided restoration. For the prompt generation, we first propose a prompt
pre-training strategy to train a frequency prompt encoder that encodes the
ground-truth image into LF and HF prompts. Then, we adopt diffusion models
(DMs) as prompt generators to generate the LF and HF prompts estimated by the
pre-trained frequency prompt encoder. For the prompt-guided restoration, we
integrate specially generated prompts into the PromptFormer network, employing
a novel Transformer-based prompt block to effectively steer the model toward
enhanced reflection removal. The results on commonly used benchmarks show that
our method outperforms state-of-the-art approaches. The codes and models are
available at this https URL

该研究提出了一种新型的 PromptRR 框架，使用频率信息作为视觉提示来改进去除反射的效果，并利用特殊生成的提示集成到 PromptFormer 网络中，采用基于 Transformer 的提示块来有效引导模型进行增强反射去除。结果表明，该方法优于现有的方法。