Single image reflection removal is inherently ambiguous, as both the
reflection and transmission components requiring separation may follow natural
image statistics. Existing methods attempt to address the issue by using
various types of low-level and physics-based cues as sources of reflection
signals. However, these cues are not universally applicable, since they are
only observable in specific capture scenarios. This leads to a significant
performance drop when test images do not align with their assumptions. In this
paper, we aim to explore a novel flexible interactive reflection removal
approach that leverages various forms of sparse human guidance, such as points
and bounding boxes, as auxiliary high-level prior to achieve robust reflection
removal. However, incorporating the raw user guidance naively into the existing
reflection removal network does not result in performance gains. To this end,
we innovatively transform raw user input into a unified form -- reflection
masks using an Interactive Segmentation Foundation Model. Such a design absorbs
the quintessence of the foundational segmentation model and flexible human
guidance, thereby mitigating the challenges of reflection separations.
Furthermore, to fully utilize user guidance and reduce user annotation costs,
we design a mask-guided reflection removal network, comprising our proposed
self-adaptive prompt block. This block adaptively incorporates user guidance as
anchors and refines transmission features via cross-attention mechanisms.
Extensive results on real-world images validate that our method demonstrates
state-of-the-art performance on various datasets with the help of flexible and
sparse user guidance. Our code and dataset will be publicly available here
this https URL

本文讨论了单幅图像去反射问题的不确定性，提出了一种基于灵活交互的去反射方法，利用稀疏人工引导作为辅助高级先验，通过转化人工输入形式并设计掩码引导的去反射网络，实现了在各种数据集上具有领先性能的反射去除。

朝着人工引导的灵活互动去除反射

Towards Flexible Interactive Reflection Removal with Human Guidance

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。

PromptRR：扩散模型作为单图反射去除的提示生成器

PromptRR: Diffusion Models as Prompt Generators for Single Image  Reflection Removal

Single Image Reflection Removal (SIRR) in real-world images is a challenging
task due to diverse image degradations occurring on the glass surface during
light transmission and reflection. Many existing methods rely on specific prior
assumptions to resolve the problem. In this paper, we propose a general
reflection intensity prior that captures the intensity of the reflection
phenomenon and demonstrate its effectiveness. To learn the reflection intensity
prior, we introduce the Reflection Prior Extraction Network (RPEN). By
segmenting images into regional patches, RPEN learns non-uniform reflection
prior in an image. We propose Prior-based Reflection Removal Network (PRRN)
using a simple transformer U-Net architecture that adapts reflection prior fed
from RPEN. Experimental results on real-world benchmarks demonstrate the
effectiveness of our approach achieving state-of-the-art accuracy in SIRR.

该论文介绍了一种用于解决实际场景中的单幅图像去反射问题的方法，通过提取反射强度先验并结合反射去除网络的设计，在真实世界的基准测试上取得了最先进的准确率。